Entering the maturity phase of agentic workflows with cost-efficient harnesses

• Intro
• Not all harnesses are born equal, nor should they be
• A Small Experiment
  • Boilerplate and common code
  • Multi Agent Driven
    • Making the comparison fair by stripping the harness of generic tools
    • Mock APIs and tools
    • Customer support agent and its refund sub-agent
    • Defining the agents and the start/resume commands
    • Let’s run the happy path
    • Observations
      • Delegation to the sub agent
      • How can the output be different than the output_token_ids?
      • Sub agent - refund agent enters the flow
      • Sub agent - tool response as an input, KV Cache kicks in
      • Hidden taxes cause 57% more inference requests
      • Continuing the flow
    • Usage Summary
  • Workflow / Orchestration Driven
    • Customer Support Graph and Refund Graph
    • Let’s run the happy path again
    • Observations
      • Significantly smaller system prompts and no mistake taxes
      • Usage Summary - reduced by a factor of 20
• Token budget is only half the story
• Key Takeaway

Intro

Almost a year and a half has passed since my last entry about agentic workflows (I really need to find some more time for this blog). As opposed to what was written more than a year ago, at the current time agents in products are not a novelty anymore, they are a commodity.

Besides the fact that agents are now expected to complete much more complex tasks and the customer tolerance to hallucinations, mistakes or errors has significantly decreased, as I see it, we’ve also entered a new phase, I like to think of it as the maturity phase.

If a year ago, a good achievement was that an agent successfully handled user interactions and completed the relevant tasks, now we started to ask harder questions. How fast did it do it, and by extension, how many resources did it consume? We’re counting them tokens, whether we pay for them with money to external inference services or by taking up GPU time on our own clusters.

As with any other technology, once it becomes commoditized, the tooling becomes easier to use, and as the tooling becomes easier to use, it also becomes easier to use it ineffectively.
Recently, we have been evaluating different optimizations for the harness we developed for our Cybersecurity Hunter agent, so I thought I would turn the some observations into a post.

I will focus on harness effectiveness, particularly on carefully aligning the capabilities of the harness with the work the agent is actually expected to perform. Obviously, I cannot share the real implementation, so I will provide a generalized and simplified example instead.

We will observe how two harnesses, each built using an industry-approved, battle-tested, production-grade tool, can lead an agent following the same happy path to consume resources that differ by a factor of 20.

Not all harnesses are born equal, nor should they be

When developing a harness to support an agentic workflow, two of the more common architectures we can choose from are:

1. Agent / Multi Agent driven - Supplying the agent with a rich set of tools and capabilities, instructing them but letting them figure out how to use them to complete the task at hand.
2. Workflow / Orchestrator driven - Curated execution paths, using LLMs in well-defined intersections in the workflow that require semantic reasoning.

Like all things in software (and life), everything’s a tradeoff.

(Just a small yet important clarification, obviously it doesn’t matter which architecture you choose, its tool usage security boundaries, authentication, authorization, permission model - are deterministic and enforced by code, not LLM. This goes without saying.)

Your choice of architecture should be derived from the complexity of what your agent should support.
If you’re shipping a general purpose harness (copilot, claude and friends) or an extremely tool-rich ecosystem like n8n, choosing an orchestration driven approach might end up in endless maintenance and edge-case chases. With that being said, in most products, an agent will have a measurable number of paths it can take within the boundaries of the ecosystem (as complex as it may be) to complete a task. And if you have a measurable number of paths, this is where the tradeoff choice between efficiency and robustness becomes tricky.

I don’t know if it’s an anecdotal bias that I’ve experienced, but most people seem to over-estimate the required complexity of their agentic workflows, choosing the agent driven architecture, not always realizing the scale of cost-ineffectiveness of their choice, especially at the time of writing these lines when token costs (or GPU prices if you run your own clusters) are increasing.

A Small Experiment

In order to make an educated decision, let’s do a small experiment to compare and break down token usage between the two approaches. We’ll create an agentic workflow of a customer support agent which needs to handle a refund request from a customer. Obviously to make it fit a blog entry, it will be an extremely simplified workflow, with only happy paths and mock tools, no validations or any production-ish safeguards.

The tested happy path for both agents will be:

Boilerplate and common code

I will add a repo with a full source code of the experiment at the end of this entry, but I will not write and explain boilerplate code which is not relevant to the core business of the experiment, to make this entry more readable and to the point.

For example, if you see the code snippets calling get_llm, assume there is such a function, or if you encounter start_flow with thread_id, assume there is something that called this function. Same goes for State classes (e.g, RefundState) in the orchestration architecture. These functions are not interesting in the context of examining the tradeoffs of the approaches and not worth the read time.

Multi Agent Driven

I’ll use langchain’s deepagents harness, but it’s just a tooling choice. It could be Copilot SDK, OpenAI SDK, Microsoft Agent Harness or any other tool.

Making the comparison fair by stripping the harness of generic tools

For the sake of making the comparison to the orchestration workflow fair in terms of token usage, I’ll strip the harness of the generic built-in tools like file CRUD operations, glob, grep, ls, write todos, execute etc’.

Code: Stripping the harness of generic tools

from deepagents import (
    GeneralPurposeSubagentProfile,
    HarnessProfile,
    register_harness_profile,
)

register_harness_profile(
    "openai",
    HarnessProfile(
        excluded_tools=({
            "write_todos",
            "ls",
            "read_file",
            "write_file",
            "edit_file",
            "glob",
            "grep",
            "execute",
        }),
        excluded_middleware=frozenset({
            "TodoListMiddleware"
        }),
        general_purpose_subagent=GeneralPurposeSubagentProfile(enabled=False),
    ),
)

Mock APIs and tools

We will supply the agent with 3 mock tools via langchain @tool decorator that should cover all the capabilities required from the agent to complete the workflow as described in the happy path above: search_orders to look up the user’s orders, refund_eligibility to check whether a refund request complies with the policy, and ask_customer for an interrupt, prompting the user for clarification on the purchase. These tools use mock APIs that simulate a database of orders and a user authentication system.

Code: Mock APIs

from datetime import datetime, timedelta


_ORDERS = {
    "user_123": [
        {"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": datetime.now() - timedelta(days=3)},
        {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": datetime.now() - timedelta(days=10)},
    ]
}

_TOKENS = {
    "token_abc": {"user_id": "user_123", "name": "John Doe", "email": "john.doe@example.com"},
}


def search_orders_by_user_id(user_id: str) -> list[dict]:
    return _ORDERS.get(user_id, [])


def check_refund_eligibility(order: dict) -> bool:
    days_since_order = (datetime.now() - order["date"]).days
    return days_since_order < 7 and order["amount"] < 150


def authorize_user_by_token(token: str) -> dict | None:
    return _TOKENS.get(token)

Note that I’ve included the docstring in each function since based on this docstring, deepagents builds the internal tool descriptions for the agent to know what to invoke and when.

Code: Mock Tools

@tool
def search_orders(runtime: ToolRuntime) -> list[dict]:

    """Search all orders placed by the authenticated customer.

    Returns the customer's orders (order_id, item, color, amount, date). The customer is
    taken from the authenticated session, not from any argument.
    """

    user_id = runtime.context.user_id
    orders = search_orders_by_user_id(user_id)

    return [
        {
            "order_id": order["order_id"],
            "item": order["item"],
            "color": order["color"],
            "amount": order["amount"],
            "date": order["date"].strftime("%Y-%m-%d"),
        }
        for order in orders
    ]


@tool
def refund_eligibility(order_id: str, runtime: ToolRuntime) -> dict:

    """Check whether one of the customer's orders is eligible for a refund.

    Given an order_id (from search_orders), returns whether it is eligible. An order is
    eligible only when placed less than 7 days ago and under $150.
    """

    user_id = runtime.context.user_id
    orders = search_orders_by_user_id(user_id)
    order = next((o for o in orders if o["order_id"] == order_id), None)

    eligible = check_refund_eligibility(order)

    return {"order_id": order_id, "eligible": eligible}


@tool
def ask_customer(question: str) -> str:
    """Ask the customer a clarifying question and wait for their answer.

    Use this when you cannot proceed without input from the customer (for example, to
    choose which of several orders to refund). Execution pauses until the answer arrives.
    """
    return interrupt(question)

Customer support agent and its refund sub-agent

This is the main point in the Multi Agent architecture: we’ll define a customer support agent and a specific refund agent as a sub-agent of the customer support agent. The customer support agent will be responsible for understanding the user’s request and delegating refund requests to the refund agent.

Let’s create simplistic system prompts for both agents to instruct them on how to handle the requests. Once again I’m reminding that this is a simplified example to fit a blog entry, these prompts will look completely different in production.

Code: System prompts

CUSTOMER_SUPPORT_PROMPT = """You are customer_support_agent, the top-level customer support assistant.

Your job is to understand what the customer wants:
- If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself.
- For anything that is not a refund, respond helpfully and briefly.

The authenticated customer's name and email are provided in the conversation context.
"""

REFUND_AGENT_PROMPT = """You are refund_agent. You handle a customer's refund request from start to finish.

You have these tools:
- search_orders(): look up every order the authenticated customer placed.
- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).
- ask_customer(question): ask the customer a question and wait for their answer.

Follow this flow:
1. Call search_orders() to retrieve the customer's orders.
2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.
3. Match the customer's answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.
4. Using the matched order, call refund_eligibility with that order's order_id.
5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).

Always base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.
"""

Defining the agents and the start/resume commands

Now we’ll define a customer support agent and a refund sub-agent using the previously created harness. We will use an in-memory checkpointer provided by langgraph and provide the agents with the mock tools we’ve defined. On top of that, we’ll create run, start, and resume commands to start user interaction and resume it after the interrupt (interrupt means user clarification).

Code: Defining the agents and start/resume commands

from deepagents import create_deep_agent

def _build_agent():
    
    refund_agent = {
        "name": REFUND_AGENT_NAME,
        "description": REFUND_AGENT_DESCRIPTION,
        "system_prompt": REFUND_AGENT_PROMPT,
        "tools": [search_orders, refund_eligibility, ask_customer],
    }

    checkpointer = InMemorySaver()
    
    return create_deep_agent(
        tools=[],
        system_prompt=CUSTOMER_SUPPORT_PROMPT,
        subagents=[refund_agent],
        model=get_llm(),
        context_schema=AgentContext,
        checkpointer=checkpointer,
    )


_AGENT = _build_agent()


def _run(payload, thread_id: str, user_id: str) -> dict:

    config: dict = {
        "configurable": {"thread_id": thread_id}
    }

    result = _AGENT.invoke(payload, config=config, context=AgentContext(user_id=user_id))

    interrupts = result.get("__interrupt__")

    if interrupts:
        return {"status": INTERRUPTED, "prompt": interrupts[0].value, "thread_id": thread_id}

    return {"status": COMPLETED, "reply": _last_ai_text(result["messages"]), "thread_id": thread_id}


def start_flow(token: str, message: str, thread_id: str) -> dict | None:

    user = authorize_user_by_token(token)

    context_message = {
        "role": "system",
        "content": f"Authenticated customer -> name: {user['name']}, email: {user['email']}.",
    }

    payload = {"messages": [context_message, {"role": "user", "content": message}]}

    return _run(payload, thread_id, user["user_id"])


def resume_flow(token: str, feedback: str, thread_id: str) -> dict | None:

    user = authorize_user_by_token(token)

    return _run(Command(resume=feedback), thread_id, user["user_id"])

Let’s run the happy path

I will run this from my python console in PyCharm:

python .\deepagents_app\run.py
 
Input: I want a refund
Output: You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?
Input: The one with the monkey picture
Output: I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?
Input: The red one
Output: Your refund has been processed successfully. If you have any other questions, let me know.

If we’ll examine the server logs (I’ve truncated irrelevant logs, generic info like PIDs and dates), we’ll see the following:

Code: Server logs

DEBUG 12:05:55 Request chatcmpl-954811fd211827c0 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 5989, 59508, 62035, 11, 290, 2344, 19231, 5989, 2498, 29186, 364, 9719, 3349, 382, 316, 4218, 1412, 290, 5989, 10648, 734, 12, 1843, 290, 5989, 382, 16054, 395, 261, 18376, 11, 28801, 290, 6062, 18376, 17491, 316, 290, 18376, 62035, 1543, 16932, 13, 3756, 625, 2273, 89728, 6675, 558, 12, 2214, 6137, 484, 382, 625, 261, 18376, 11, 9570, 1652, 5203, 326, 51088, 364, 976, 76990, 122862, 1308, 326, 3719, 553, 5181, 306, 290, 15681, 3814, 8525, 3575, 553, 261, 8103, 11793, 11, 448, 20837, 29186, 484, 9335, 5385, 24226, 13638, 2360, 8437, 13, 1608, 9570, 483, 2201, 326, 4584, 11666, 13, 623, 1825, 665, 1921, 634, 22488, 326, 4584, 32725, 306, 1374, 1058, 364, 877, 16309, 53471, 279, 12, 2439, 82463, 326, 2823, 13, 19666, 1072, 15801, 26330, 12604, 7747, 558, 12, 82368, 1147, 42963, 876, 47712, 7109, 62915, 35854, 392, 19936, 4928, 35854, 392, 67504, 1954, 1008, 88948, 12, 19666, 2891, 392, 67504, 1954, 621, 2127, 1, 2733, 1327, 621, 480, 558, 12, 1843, 290, 2616, 382, 35769, 98171, 11, 3810, 1606, 290, 11085, 2622, 817, 6118, 316, 2304, 290, 2613, 8316, 3736, 558, 12, 1843, 7747, 1495, 316, 7139, 3543, 11, 16644, 1577, 11, 1815, 1330, 364, 877, 21768, 4677, 2409, 279, 12, 39936, 90696, 18580, 1072, 139562, 290, 49366, 36472, 198, 12, 3946, 152061, 175017, 1261, 290, 1825, 382, 25570, 198, 12, 46613, 42963, 2539, 75, 8015, 11, 44485, 11, 503, 19864, 19618, 279, 877, 65680, 86043, 279, 5958, 290, 1825, 31064, 481, 316, 621, 3543, 1402, 16, 13, 6240, 177543, 1577, 410, 2733, 1729, 12331, 6291, 11, 2371, 9595, 18587, 13, 23584, 889, 21182, 2733, 13660, 4951, 11716, 316, 1604, 11, 1815, 63166, 558, 17, 13, 6240, 2818, 410, 2733, 6365, 290, 7578, 13, 6823, 8065, 889, 44116, 558, 18, 13, 6240, 37762, 410, 2733, 2371, 634, 1101, 4372, 1412, 673, 7747, 11, 625, 4372, 634, 2316, 4733, 13, 4886, 1577, 8704, 382, 32208, 6145, 2733, 63166, 364, 25627, 4113, 4609, 290, 5296, 382, 9637, 5533, 13, 19666, 5666, 997, 3499, 326, 16644, 1412, 481, 1481, 621, 2733, 1327, 621, 480, 13, 12817, 14376, 1602, 316, 290, 1825, 1261, 290, 5296, 382, 4167, 503, 7163, 50535, 35275, 364, 410, 5958, 3283, 810, 8201, 25, 91587, 12, 1843, 3543, 28336, 45605, 11, 5666, 326, 30532, 425, 60291, 9, 2733, 4128, 3357, 45364, 289, 290, 2684, 7139, 558, 12, 1843, 7163, 35275, 11, 5485, 290, 1825, 29400, 8201, 326, 3810, 395, 21344, 364, 877, 54315, 13700, 94520, 279, 12, 3756, 625, 3810, 395, 4878, 290, 1825, 4279, 26695, 558, 12, 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38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 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200007, 200006, 77944, 200008, 2, 68406, 279, 47259, 5989, 2747, 1308, 25, 5928, 78200, 11, 3719, 25, 64626, 1380, 5578, 81309, 1136, 13, 200007, 200006, 1428, 200008, 40, 1682, 261, 18376, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:05:56 Generated response chatcmpl-954811fd211827c0: output: '[tool_calls: task({"description": "Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.", "subagent_type": "refund_agent"})]', output_token_ids: [200005, 35644, 200008, 1844, 5003, 392, 40, 1682, 261, 18376, 3692, 1416, 2804, 28801, 316, 18376, 62035, 13, 1877, 777, 15543, 25, 392, 3335, 290, 5989, 382, 16054, 395, 261, 18376, 11, 28801, 290, 6062, 18376, 17491, 316, 290, 18376, 62035, 1543, 16932, 13, 3756, 625, 2273, 89728, 6675, 3692, 2632, 581, 2421, 290, 5296, 4584, 11, 1543, 16932, 3804, 25, 18376, 62035, 11, 6496, 25, 21924, 484, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 27230, 220, 200003, 4108, 200008, 10848, 9186, 7534, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 85388, 3834, 16932, 3804, 7534, 148482, 62035, 18583, 200012], finish_reason: tool_calls

DEBUG 12:05:56 Request chatcmpl-b7f719eec91c8a93 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 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5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:05:57 Generated response chatcmpl-b7f719eec91c8a93: output: '[tool_calls: search_orders({})]', output_token_ids: [200005, 35644, 200008, 976, 1825, 10648, 765, 316, 5318, 290, 18376, 395, 290, 76990, 5989, 5928, 78200, 13, 1416, 1309, 316, 2622, 290, 7845, 25, 2421, 3684, 88420, 1577, 13, 7801, 538, 7598, 12528, 11, 3810, 5989, 1118, 1001, 13, 7801, 3981, 6052, 316, 1001, 328, 12528, 13, 1843, 59698, 11, 3810, 2418, 13, 7801, 2421, 18376, 62, 15570, 4295, 13, 7801, 9570, 44041, 364, 2167, 1309, 316, 2421, 3684, 88420, 350, 1750, 11917, 8, 326, 717, 290, 10508, 12528, 13, 7801, 9570, 30, 5551, 11, 581, 1309, 316, 621, 25383, 10331, 13, 46830, 2421, 3684, 88420, 13, 41021, 621, 484, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 16718, 88420, 220, 200003, 4108, 200008, 12083, 200012], finish_reason: tool_calls

DEBUG 12:05:57 Request chatcmpl-8fd5565575485dd2 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:05:59 Generated response chatcmpl-8fd5565575485dd2: output: '[tool_calls: ask_customer<|channel|>commentary({"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: [200005, 35644, 200008, 2167, 1309, 316, 3810, 5989, 538, 51676, 7598, 12528, 13, 3274, 553, 1920, 12528, 13, 16344, 316, 7845, 11, 581, 2804, 3810, 1373, 316, 5230, 1118, 1001, 316, 18376, 13, 2632, 3810, 4928, 17040, 2454, 5317, 483, 2169, 11, 3089, 11, 4527, 11, 3521, 13, 43810, 6052, 59674, 4857, 2569, 364, 106232, 3810, 25, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 220, 200003, 4108, 200008, 10848, 14921, 7534, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012], finish_reason: tool_calls

DEBUG 12:05:59 Request chatcmpl-9c43ae7feb521ed4 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:01 Generated response chatcmpl-9c43ae7feb521ed4: output: '[tool_calls: ask_customer({"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: [200005, 35644, 200008, 2167, 38398, 316, 1199, 3810, 65588, 87633, 30, 50557, 581, 2061, 3810, 65588, 3518, 461, 44580, 3552, 28, 44580, 133800, 65588, 1008, 127222, 623, 64882, 25, 581, 1309, 316, 2421, 3810, 65588, 1114, 13, 21926, 1299, 354, 14921, 25, 1621, 16255, 2632, 1757, 413, 1402, 168394, 4108, 198, 745, 1, 14921, 1243, 392, 63347, 739, 26178, 38193, 7943, 581, 1217, 1309, 316, 27018, 4584, 1308, 13, 730, 5880, 3490, 581, 2061, 392, 44580, 133800, 65588, 1, 25435, 13, 623, 2915, 5003, 392, 1447, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 3692, 21926, 2236, 581, 87633, 114467, 8817, 91, 21453, 91, 29, 12606, 815, 4050, 1416, 1757, 6462, 2421, 290, 1114, 13, 46830, 2421, 8516, 25, 316, 28, 44580, 133800, 65588, 364, 84787, 25, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 133800, 65588, 200003, 4108, 200008, 10848, 14921, 7534, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012], finish_reason: tool_calls

DEBUG 12:06:08 Request chatcmpl-bfccc4239e937865 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 1001, 483, 290, 73454, 8723, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:10 Generated response chatcmpl-bfccc4239e937865: output: '[tool_calls: ask_customer<|channel|>commentary({"question": "I\\u2019m sorry, I don\\u2019t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: [200005, 35644, 200008, 976, 5989, 34121, 25, 392, 976, 1001, 483, 290, 73454, 8723, 3692, 5551, 11, 12528, 581, 679, 25, 3592, 27628, 11, 9861, 27628, 13, 4662, 8633, 261, 73454, 8723, 13, 2632, 860, 3981, 13, 1416, 1309, 316, 64461, 483, 3810, 65588, 2418, 11, 2356, 3880, 13, 43810, 581, 3981, 9707, 1001, 2569, 13, 9493, 123648, 13, 2632, 581, 2421, 3810, 65588, 2418, 17040, 3880, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 200003, 4108, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012], finish_reason: tool_calls

DEBUG 12:06:10 Request chatcmpl-816b2267f0ee7489 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 1001, 483, 290, 73454, 8723, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:12 Generated response chatcmpl-816b2267f0ee7489: output: '[tool_calls: ask_customer<|channel|>commentary({"question": "I\\u2019m sorry, I don\\u2019t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: [200005, 35644, 200008, 976, 2420, 25, 1416, 2804, 3810, 2360, 3810, 65588, 2418, 13, 623, 2174, 3810, 65588, 2421, 625, 12331, 13, 1416, 3508, 3322, 25, 392, 976, 1001, 483, 290, 73454, 8723, 4050, 3004, 3981, 13, 18057, 3810, 2418, 483, 3880, 364, 2167, 1309, 316, 2622, 7845, 25, 3810, 65588, 2418, 4609, 5364, 3981, 13, 17291, 3810, 2418, 13, 7801, 1934, 3981, 11, 2371, 57418, 364, 5808, 581, 2804, 2421, 3810, 65588, 25, 392, 976, 12528, 553, 2550, 21580, 1481, 481, 1299, 316, 18376, 16842, 41021, 621, 2418, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 200003, 4108, 200008, 10848, 14921, 7534, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012], finish_reason: tool_calls

DEBUG 12:06:12 Request chatcmpl-ab8440d5fcd521c1 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 1001, 483, 290, 73454, 8723, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:15 Generated response chatcmpl-ab8440d5fcd521c1: output: '[tool_calls: ask_customer({"question": "I\\u2019m sorry, I don\\u2019t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: [200005, 35644, 200008, 2167, 3508, 261, 11453, 328, 3810, 65588, 11666, 5192, 316, 145860, 328, 45440, 13, 50557, 1309, 316, 2421, 3810, 65588, 326, 1815, 9570, 483, 290, 122862, 6052, 13, 623, 2420, 80970, 889, 581, 553, 1327, 17554, 162016, 26, 581, 1309, 316, 3810, 4928, 11, 889, 581, 738, 717, 860, 6052, 13, 730, 495, 5296, 11, 581, 553, 316, 66687, 22391, 483, 1825, 13, 623, 1825, 9289, 9570, 26, 581, 1309, 316, 3810, 13, 7801, 1934, 581, 717, 122862, 6052, 11, 581, 2622, 364, 2167, 1309, 316, 5238, 395, 290, 5989, 316, 16794, 13, 357, 2411, 581, 665, 19429, 484, 306, 30937, 11, 290, 1825, 738, 9570, 306, 290, 2613, 3176, 13, 16354, 1954, 581, 7747, 889, 581, 3508, 290, 1825, 802, 6052, 25, 392, 976, 1001, 483, 290, 73454, 8723, 4050, 4569, 673, 591, 290, 2915, 4733, 30, 24305, 25, 623, 29186, 885, 2316, 2421, 1458, 2915, 392, 1447, 65588, 382, 625, 261, 4529, 4584, 1, 889, 581, 8274, 290, 49366, 6052, 3992, 30, 623, 2142, 7398, 290, 1825, 10175, 392, 976, 1001, 483, 290, 73454, 8723, 4050, 4569, 7890, 1299, 261, 3322, 316, 1039, 2174, 4928, 13, 2632, 581, 679, 448, 6052, 11, 889, 581, 1309, 316, 3981, 316, 290, 12528, 13, 392, 976, 1001, 483, 290, 73454, 8723, 1, 8740, 3981, 4784, 13, 1416, 1309, 316, 64461, 2418, 13, 41021, 621, 484, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 133800, 65588, 200003, 4108, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012], finish_reason: tool_calls

DEBUG 12:06:27 Request chatcmpl-9cf5290a4efbb34c details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 1001, 483, 290, 73454, 8723, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 3592, 1001, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:28 Generated response chatcmpl-9cf5290a4efbb34c: output: '[tool_calls: refund_eligibility<|channel|>commentary({"order_id": "order_001"})]', output_token_ids: [200005, 35644, 200008, 2167, 3508, 290, 6052, 25, 392, 976, 3592, 1001, 4050, 1416, 1309, 316, 3981, 316, 2569, 25, 2569, 62, 7659, 382, 3592, 27628, 13, 2632, 10954, 2569, 1537, 2569, 62, 7659, 13, 7801, 2371, 18376, 57418, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 200005, 12606, 815, 200003, 4108, 200008, 10848, 2143, 1537, 7534, 2143, 62, 7659, 18583, 200012], finish_reason: tool_calls

DEBUG 12:06:28 Request chatcmpl-9b19bc0b1f141601 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 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1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 1001, 483, 290, 73454, 8723, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 3592, 1001, 200007, 200006, 173781, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 18583, 200012, 200006, 44580, 18976, 980, 62, 15570, 4295, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 18376, 62, 15570, 4295, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:29 Generated response chatcmpl-9b19bc0b1f141601: output: '[tool_calls: refund_eligibility({"order_id": "order_001"})]', output_token_ids: [200005, 35644, 200008, 2167, 2452, 29348, 25, 9289, 1853, 11917, 20323, 30, 1416, 1309, 316, 2421, 9964, 18976, 980, 62, 15570, 4295, 483, 9621, 13, 3673, 2421, 45440, 25, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 279, 58369, 6145, 13, 8820, 5880, 3810, 65588, 7598, 11666, 13, 623, 5989, 28446, 392, 3086, 3592, 1001, 4050, 1416, 1309, 316, 3981, 9707, 316, 2569, 62, 7659, 13, 4569, 382, 5364, 13, 6549, 2421, 18376, 62, 15570, 4295, 483, 2569, 1537, 25, 2569, 62, 7659, 13, 3072, 581, 1458, 2915, 14471, 13, 1416, 1309, 316, 621, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 2935, 464, 12606, 815, 34397, 41021, 109462, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 200003, 4108, 200008, 10848, 2143, 1537, 7534, 2143, 62, 7659, 18583, 200012], finish_reason: tool_calls

DEBUG 12:06:29 Request chatcmpl-b95b4be03f7ab272 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 18376, 62035, 13, 1608, 5318, 261, 122862, 18376, 2616, 591, 1604, 316, 9115, 364, 3575, 679, 1879, 8437, 734, 12, 3684, 88420, 9442, 1631, 869, 1753, 2569, 290, 76990, 5989, 12989, 558, 12, 18376, 62, 15570, 4295, 33050, 1537, 3127, 2371, 538, 261, 4857, 2569, 665, 413, 109969, 350, 40927, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 6294, 12, 3810, 65588, 78997, 3127, 3810, 290, 5989, 261, 4928, 326, 5238, 395, 1043, 6052, 364, 16086, 495, 7845, 734, 16, 13, 9238, 3684, 88420, 416, 316, 31053, 290, 122862, 12528, 558, 17, 13, 1843, 945, 1572, 1001, 2569, 382, 10508, 11, 621, 7116, 11915, 13, 9238, 3810, 65588, 483, 261, 4928, 484, 19471, 2454, 5317, 350, 2057, 11, 3089, 11, 4527, 326, 3521, 8, 326, 31064, 1118, 1001, 316, 18376, 558, 18, 13, 20501, 290, 122862, 6052, 316, 9707, 1001, 328, 290, 12528, 10508, 656, 3684, 88420, 1454, 623, 6052, 665, 413, 2240, 2201, 350, 64, 2086, 11, 448, 2569, 1537, 11, 503, 261, 6496, 1299, 392, 3086, 9861, 1001, 3172, 1843, 290, 6052, 2226, 7116, 15603, 3981, 1062, 328, 2617, 12528, 11, 621, 7116, 5230, 1001, 22332, 25, 2421, 3810, 65588, 2418, 316, 64461, 11, 14253, 290, 2839, 3880, 13, 12817, 4901, 4730, 290, 6052, 59674, 261, 4857, 2569, 558, 19, 13, 17511, 290, 31366, 2569, 11, 2421, 18376, 62, 15570, 4295, 483, 484, 2569, 885, 2569, 1537, 558, 20, 13, 1843, 480, 382, 21680, 11, 5485, 290, 5989, 290, 18376, 673, 23478, 12836, 13, 1843, 480, 382, 625, 21680, 11, 16644, 4436, 350, 23657, 2890, 503, 3101, 14818, 3991, 48258, 3611, 634, 14678, 402, 290, 4584, 32725, 11, 3779, 402, 58384, 13, 28741, 18376, 448, 2569, 290, 5989, 2242, 625, 4771, 5655, 8525, 877, 34858, 1532, 38066, 279, 12, 5405, 6291, 2254, 24193, 2733, 4218, 9595, 3100, 2254, 4137, 6629, 198, 12, 134158, 291, 9595, 2713, 11, 64882, 83311, 11, 326, 18587, 279, 877, 32277, 839, 20574, 2700, 3973, 15007, 2700, 1293, 5933, 15007, 2700, 9566, 5933, 15007, 2700, 6360, 5933, 15007, 2700, 107649, 15007, 2700, 81938, 38193, 3575, 679, 3158, 316, 261, 105930, 1118, 481, 665, 20255, 483, 2360, 1879, 8437, 558, 2594, 1974, 23373, 2804, 1604, 483, 261, 34801, 17547, 290, 4584, 53175, 395, 290, 2839, 8437, 11, 326, 1199, 54807, 350, 6680, 14, 19698, 8, 1261, 6085, 4410, 6291, 364, 12, 41498, 25, 1562, 6291, 306, 261, 12552, 350, 93000, 17786, 3104, 446, 12, 1729, 5933, 25, 1729, 261, 1974, 591, 290, 105930, 198, 12, 5067, 5933, 25, 5067, 316, 261, 1974, 306, 290, 105930, 198, 12, 9204, 5933, 25, 9204, 261, 1974, 306, 290, 105930, 198, 12, 19753, 25, 1646, 6291, 20238, 261, 8302, 350, 68, 1940, 4213, 165557, 52697, 5823, 1896, 12, 51548, 25, 3684, 395, 2201, 3518, 6291, 279, 877, 27976, 19778, 26676, 279, 5958, 261, 4584, 1534, 382, 3101, 4410, 11, 480, 1340, 413, 1277, 33483, 1511, 290, 105930, 7665, 328, 2447, 10508, 15905, 13, 730, 2617, 7911, 11, 1199, 2700, 1293, 5933, 63, 316, 36665, 290, 10576, 1534, 306, 53440, 11, 503, 1199, 2700, 81938, 63, 3518, 70699, 32551, 53637, 31049, 14, 63, 538, 481, 1309, 316, 3684, 5251, 1277, 33483, 4584, 4376, 326, 621, 625, 1761, 290, 6354, 1974, 3104, 13, 5011, 33483, 4584, 4376, 553, 16240, 1641, 70699, 32551, 53637, 31049, 52517, 17952, 25158, 1537, 88780, 364, 2, 20574, 279, 877, 9964, 279, 4797, 9964, 95359, 10497, 722, 12528, 12989, 656, 290, 76990, 5989, 7621, 793, 393, 257, 9609, 290, 122862, 12528, 350, 2143, 1537, 11, 2169, 11, 3089, 11, 4527, 11, 3521, 741, 623, 5989, 382, 198, 393, 257, 6697, 591, 290, 76990, 6223, 11, 625, 591, 1062, 10383, 558, 2493, 3684, 88420, 314, 11350, 25, 405, 9263, 871, 1062, 20544, 6208, 5588, 1001, 328, 290, 122862, 12528, 382, 21680, 395, 261, 18376, 7621, 793, 393, 257, 29844, 448, 2569, 1537, 350, 2845, 3684, 88420, 936, 7377, 5588, 480, 382, 21680, 13, 1689, 2569, 382, 198, 393, 257, 21680, 1606, 1261, 12989, 3760, 1572, 220, 22, 3376, 5288, 326, 1641, 548, 5215, 558, 2493, 18376, 62, 15570, 4295, 314, 11350, 25, 405, 2143, 1537, 25, 1621, 412, 9263, 871, 1062, 20544, 23600, 290, 5989, 261, 16702, 13700, 4928, 326, 5238, 395, 1043, 6052, 7621, 793, 393, 257, 7649, 495, 1261, 481, 6284, 18988, 2935, 3422, 591, 290, 5989, 350, 1938, 4994, 11, 316, 198, 393, 257, 5655, 1118, 328, 4919, 12528, 316, 18376, 741, 58390, 179917, 4609, 290, 6052, 39650, 558, 2493, 3810, 65588, 314, 11350, 25, 405, 14921, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 1428, 200008, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 13, 200007, 200006, 173781, 316, 28, 44580, 16718, 88420, 200005, 12606, 815, 5701, 200008, 12083, 200012, 200006, 44580, 16718, 88420, 316, 28, 173781, 200005, 12606, 815, 200008, 58, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 2057, 1243, 392, 1291, 27628, 672, 392, 4991, 1243, 392, 1291, 672, 392, 10703, 1243, 220, 1353, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 2270, 37834, 10494, 2143, 1537, 1243, 392, 2143, 62, 11950, 672, 392, 2057, 1243, 392, 18789, 27628, 672, 392, 4991, 1243, 392, 18789, 672, 392, 10703, 1243, 220, 7521, 11, 392, 1319, 1243, 392, 1323, 21, 12, 3173, 12, 1709, 18583, 60, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 62915, 13, 1608, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 3575, 679, 1920, 12528, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 1001, 483, 290, 73454, 8723, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 3810, 65588, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 133800, 65588, 200005, 12606, 815, 5701, 200008, 10848, 14921, 1243, 392, 40, 4572, 23045, 11, 357, 1700, 1573, 1921, 261, 73454, 8723, 306, 634, 12528, 13, 623, 2839, 3880, 553, 25, 220, 16, 8, 5952, 27628, 350, 1291, 11, 548, 1353, 11, 12989, 220, 1323, 21, 12, 3173, 12, 2270, 936, 220, 17, 8, 11942, 27628, 350, 18789, 11, 548, 7521, 11, 12989, 220, 1323, 21, 12, 3173, 12, 1709, 741, 21580, 1481, 481, 1299, 316, 18376, 16842, 92, 200012, 200006, 44580, 133800, 65588, 316, 28, 173781, 200005, 12606, 815, 200008, 976, 3592, 1001, 200007, 200006, 173781, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 27, 91, 21453, 91, 29, 12606, 815, 200005, 12606, 815, 5701, 200008, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 18583, 200012, 200006, 44580, 18976, 980, 62, 15570, 4295, 27, 91, 21453, 91, 29, 12606, 815, 316, 28, 173781, 200005, 12606, 815, 200008, 2255, 25, 18376, 62, 15570, 4295, 27, 91, 21453, 91, 29, 12606, 815, 382, 625, 261, 4529, 4584, 11, 2075, 1001, 328, 723, 3973, 11, 1729, 5933, 11, 5067, 5933, 11, 9204, 5933, 11, 19753, 11, 51548, 11, 15792, 11, 3684, 88420, 11, 18376, 62, 15570, 4295, 11, 3810, 65588, 1651, 200007, 200006, 173781, 316, 28, 44580, 18976, 980, 62, 15570, 4295, 200005, 12606, 815, 5701, 200008, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 18583, 200012, 200006, 44580, 18976, 980, 62, 15570, 4295, 316, 28, 173781, 200005, 12606, 815, 200008, 10848, 2143, 1537, 1243, 392, 2143, 62, 7659, 672, 392, 116015, 1243, 1343, 92, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:30 Generated response chatcmpl-b95b4be03f7ab272: output: 'Your red shirt (order_001) is eligible for a refund. The refund has been processed successfully. If you need anything else, just let me know!', output_token_ids: [200005, 35644, 200008, 10620, 9570, 13, 200007, 200006, 173781, 200005, 17196, 200008, 9719, 3592, 27628, 350, 2143, 62, 7659, 8, 382, 21680, 395, 261, 18376, 13, 623, 18376, 853, 1339, 23478, 12836, 13, 1843, 481, 1309, 6137, 1203, 11, 1327, 1632, 668, 1761, 0, 200002], finish_reason: stop

DEBUG 12:06:30 Request chatcmpl-b14651add9f0ff42 details: prompt: None, prompt_token_ids: [200006, 17360, 200008, 3575, 553, 17554, 162016, 11, 261, 4410, 6439, 2359, 22203, 656, 7788, 17527, 558, 87447, 100594, 25, 220, 1323, 19, 12, 3218, 198, 6576, 3521, 25, 220, 1323, 21, 12, 3062, 12, 2290, 279, 30377, 289, 25, 14093, 279, 2, 13888, 18403, 25, 8450, 11, 49159, 11, 1721, 13, 21030, 2804, 413, 7360, 395, 1753, 3176, 558, 63446, 316, 1879, 8437, 2804, 810, 316, 290, 49159, 9334, 25, 461, 44580, 6120, 200007, 200006, 77944, 200008, 2, 68406, 279, 3575, 553, 5989, 59508, 62035, 11, 290, 2344, 19231, 5989, 2498, 29186, 364, 9719, 3349, 382, 316, 4218, 1412, 290, 5989, 10648, 734, 12, 1843, 290, 5989, 382, 16054, 395, 261, 18376, 11, 28801, 290, 6062, 18376, 17491, 316, 290, 18376, 62035, 1543, 16932, 13, 3756, 625, 2273, 89728, 6675, 558, 12, 2214, 6137, 484, 382, 625, 261, 18376, 11, 9570, 1652, 5203, 326, 51088, 364, 976, 76990, 122862, 1308, 326, 3719, 553, 5181, 306, 290, 15681, 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402, 495, 10901, 7621, 29186, 25, 69986, 290, 10148, 4584, 316, 11542, 483, 290, 4176, 12, 15134, 783, 11793, 11, 9072, 14633, 15543, 1078, 1412, 4176, 316, 9419, 326, 1412, 6011, 290, 3019, 1757, 2304, 198, 393, 1040, 18582, 40050, 793, 393, 464, 18582, 40050, 1825, 25, 392, 13225, 46547, 464, 12606, 815, 40050, 12265, 290, 1825, 382, 64790, 11, 1199, 290, 64790, 18391, 25947, 11793, 316, 9570, 483, 261, 11888, 41751, 198, 393, 1040, 12606, 815, 40050, 29186, 25, 392, 15390, 2966, 316, 1199, 290, 10148, 4584, 316, 11542, 483, 290, 64790, 18391, 25947, 11793, 46547, 1040, 18582, 523, 2493, 5296, 314, 11350, 25, 10168, 355, 14633, 6496, 328, 290, 5296, 395, 290, 1543, 16932, 316, 3347, 37793, 10701, 13, 50210, 722, 8155, 3814, 326, 27018, 290, 5930, 4733, 6011, 558, 9186, 25, 1621, 20046, 623, 1490, 328, 1543, 16932, 316, 1199, 13, 20953, 413, 1001, 328, 290, 2839, 11793, 6009, 12073, 306, 290, 4584, 6496, 558, 3834, 16932, 3804, 25, 1621, 412, 9263, 871, 1062, 502, 92, 602, 9819, 9964, 200007, 200006, 77944, 200008, 2, 68406, 279, 47259, 5989, 2747, 1308, 25, 5928, 78200, 11, 3719, 25, 64626, 1380, 5578, 81309, 1136, 13, 200007, 200006, 1428, 200008, 40, 1682, 261, 18376, 200007, 200006, 173781, 316, 28, 44580, 27230, 200005, 12606, 815, 5701, 200008, 10848, 9186, 1243, 392, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 21161, 392, 3834, 16932, 3804, 1243, 392, 148482, 62035, 18583, 200012, 200006, 44580, 27230, 316, 28, 173781, 200005, 12606, 815, 200008, 9719, 3592, 27628, 350, 2143, 62, 7659, 8, 382, 21680, 395, 261, 18376, 13, 623, 18376, 853, 1339, 23478, 12836, 13, 1843, 481, 1309, 6137, 1203, 11, 1327, 1632, 668, 1761, 0, 200007, 200006, 173781], prompt_embeds shape: None.

INFO 12:06:32 Generated response chatcmpl-b14651add9f0ff42: output: 'Your refund has been processed successfully. If you have any other questions, let me know.', output_token_ids: [200005, 35644, 200008, 976, 18376, 62035, 10508, 261, 3176, 484, 290, 18376, 853, 1339, 23478, 13, 3072, 581, 553, 79758, 625, 316, 2273, 89728, 18005, 13, 1416, 1757, 625, 2891, 484, 581, 23478, 480, 13, 623, 18376, 62035, 30830, 326, 28603, 23478, 13, 3072, 472, 777, 21179, 25, 1843, 290, 5989, 382, 16054, 395, 261, 18376, 11, 28801, 290, 6062, 18376, 17491, 316, 290, 18376, 62035, 1543, 16932, 13, 3756, 625, 2273, 89728, 6675, 13, 2632, 581, 1327, 5485, 290, 1825, 484, 18376, 382, 2447, 23478, 30, 623, 18376, 62035, 82769, 29594, 480, 13, 623, 1543, 16932, 10508, 261, 3176, 13, 1416, 1757, 23892, 484, 290, 18376, 853, 1339, 23478, 12836, 30, 50557, 290, 15543, 2891, 25, 621, 625, 2273, 89728, 6675, 13, 1416, 4358, 290, 1543, 16932, 13, 1225, 23478, 13, 2632, 581, 665, 60698, 1534, 13, 623, 1825, 1327, 2059, 392, 40, 1682, 261, 18376, 4050, 1416, 665, 9570, 25, 46776, 13, 12287, 13, 16835, 82463, 13, 2632, 1721, 6052, 25, 46059, 18376, 23478, 13, 200007, 200006, 173781, 200005, 17196, 200008, 9719, 18376, 853, 1339, 23478, 12836, 13, 1843, 481, 679, 1062, 1273, 5359, 11, 1632, 668, 1761, 13, 200002], finish_reason: stop

In order to make it clear, let’s decode these tokens into text.
I’ll use tiktoken and since we’re using GPT-OSS, I’ll use the o200k_harmony encoding.
If you’re not familiar with how encoding per model works, what are model specific special tokens etc’, it’s a nice read but irrelevant for the context of this entry.

So our decoding code is a simple 4 liner:

Code: Decoding the tokens

import tiktoken
enc = tiktoken.get_encoding("o200k_harmony")
ids = [...tokens] 
print(enc.decode([t for t in ids]))

If we use it to fully decode the logs we’ve seen, we’ll see the following:

Code: Decoded logs

DEBUG 12:05:55 Request chatcmpl-954811fd211827c0 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are customer_support_agent, the top-level customer support assistant.\n\nYour job is to understand what the customer wants:\n- If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself.\n- For anything that is not a refund, respond helpfully and briefly.\n\nThe authenticated customer\'s name and email are provided in the conversation context.\n\n\nYou are a deep agent, an AI assistant that helps users accomplish tasks using tools. You respond with text and tool calls. The user can see your responses and tool outputs in real time.\n\n## Core Behavior\n\n- Be concise and direct. Don\'t over-explain unless asked.\n- NEVER add unnecessary preamble ("Sure!", "Great question!", "I\'ll now...").\n- Don\'t say "I\'ll now do X" — just do it.\n- If the request is underspecified, ask only the minimum followup needed to take the next useful action.\n- If asked how to approach something, explain first, then act.\n\n## Professional Objectivity\n\n- Prioritize accuracy over validating the user\'s beliefs\n- Disagree respectfully when the user is incorrect\n- Avoid unnecessary superlatives, praise, or emotional validation\n\n## Doing Tasks\n\nWhen the user asks you to do something:\n\n1. **Understand first** — read relevant files, check existing patterns. Quick but thorough — gather enough evidence to start, then iterate.\n2. **Act** — implement the solution. Work quickly but accurately.\n3. **Verify** — check your work against what was asked, not against your own output. Your first attempt is rarely correct — iterate.\n\nKeep working until the task is fully complete. Don\'t stop partway and explain what you would do — just do it. Only yield back to the user when the task is done or you\'re genuinely blocked.\n\n**When things go wrong:**\n\n- If something fails repeatedly, stop and analyze *why* — don\'t keep retrying the same approach.\n- If you\'re blocked, tell the user what\'s wrong and ask for guidance.\n\n## Clarifying Requests\n\n- Do not ask for details the user already supplied.\n- Use reasonable defaults when the request clearly implies them.\n- Prioritize missing semantics like content, delivery, detail level, or alert criteria.\n- Avoid opening with a long explanation of tool, scheduling, or integration limitations when a concise blocking followup question would move the task forward.\n- Ask domain-defining questions before implementation questions.\n- For monitoring or alerting requests, ask what signals, thresholds, or conditions should trigger an alert.\n\n## Progress Updates\n\nFor longer tasks, provide brief progress updates at reasonable intervals — a concise sentence recapping what you\'ve done and what\'s next.\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n## `task` (subagent spawner)\n\nYou have access to a `task` tool to launch short-lived subagents that handle isolated tasks. These agents are ephemeral — they live only for the duration of the task and return a single result.\n\nWhen to use the task tool:\n\n- When a task is complex and multi-step, and can be fully delegated in isolation\n- When a task is independent of other tasks and can run in parallel\n- When a task requires focused reasoning or heavy token/context usage that would bloat the orchestrator thread\n- When sandboxing improves reliability (e.g. code execution, structured searches, data formatting)\n- When you only care about the output of the subagent, and not the intermediate steps (ex. performing a lot of research and then returned a synthesized report, performing a series of computations or lookups to achieve a concise, relevant answer.)\n\nSubagent lifecycle:\n\n1. **Spawn** → Provide clear role, instructions, and expected output\n2. **Run** → The subagent completes the task autonomously\n3. **Return** → The subagent provides a single structured result\n4. **Reconcile** → Incorporate or synthesize the result into the main thread\n\nWhen NOT to use the task tool:\n\n- If you need to see the intermediate reasoning or steps after the subagent has completed (the task tool hides them)\n- If the task is trivial (a few tool calls or simple lookup)\n- If delegating does not reduce token usage, complexity, or context switching\n- If splitting would add latency without benefit\n\n## Important Task Tool Usage Notes to Remember\n\n- Whenever possible, parallelize the work that you do. This is true for both tool_calls, and for tasks. Whenever you have independent steps to complete - make tool_calls, or kick off tasks (subagents) in parallel to accomplish them faster. This saves time for the user, which is incredibly important.\n- Remember to use the `task` tool to silo independent tasks within a multi-part objective.\n- You should use the `task` tool whenever you have a complex task that will take multiple steps, and is independent from other tasks that the agent needs to complete. These agents are highly competent and efficient.\n\nAvailable subagent types:\n\n- refund_agent: Handles the end-to-end refund request flow: finds the customer\'s orders, clarifies which order to refund, checks eligibility and confirms the refund.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Launch an ephemeral subagent to handle complex, multi-step independent tasks with isolated context windows.\n// \n// Available agent types and the tools they have access to:\n// - refund_agent: Handles the end-to-end refund request flow: finds the customer\'s orders, clarifies which order to refund, checks eligibility and confirms the refund.\n// \n// When using the Task tool, you must specify a subagent_type parameter to select which agent type to use.\n// \n// ## Usage notes:\n// 1. Launch multiple agents concurrently whenever possible, to maximize performance; to do that, use a single message with multiple tool uses\n// 2. When the agent is done, it will return a single message back to you. The result returned by the agent is not visible to the user. To show the user the result, you should send a text message back to the user with a concise summary of the result.\n// 3. Each agent invocation is stateless. You will not be able to send additional messages to the agent, nor will the agent be able to communicate with you outside of its final report. Therefore, your prompt should contain a highly detailed task description for the agent to perform autonomously and you should specify exactly what information the agent should return back to you in its final and only message to you.\n// 4. The agent\'s outputs should generally be trusted\n// 5. Clearly tell the agent whether you expect it to create content, perform analysis, or just do research (search, file reads, web fetches, etc.), since it is not aware of the user\'s intent\n// 6. If the agent description mentions that it should be used proactively, then you should try your best to use it without the user having to ask for it first. Use your judgement.\n// 7. When only the general-purpose agent is provided, you should use it for all tasks. It is great for isolating context and token usage, and completing specific, complex tasks, as it has all the same capabilities as the main agent.\n// \n// ### Example usage of the general-purpose agent:\n// \n// <example_agent_descriptions>\n// "general-purpose": use this agent for general purpose tasks, it has access to all tools as the main agent.\n// </example_agent_descriptions>\n// \n// <example>\n// User: "I want to conduct research on the accomplishments of Lebron James, Michael Jordan, and Kobe Bryant, and then compare them."\n// Assistant: *Uses the task tool in parallel to conduct isolated research on each of the three players*\n// Assistant: *Synthesizes the results of the three isolated research tasks and responds to the User*\n// <commentary>\n// Research is a complex, multi-step task in it of itself.\n// The research of each individual player is not dependent on the research of the other players.\n// The assistant uses the task tool to break down the complex objective into three isolated tasks.\n// Each research task only needs to worry about context and tokens about one player, then returns synthesized information about each player as the Tool Result.\n// This means each research task can dive deep and spend tokens and context deeply researching each player, but the final result is synthesized information, and saves us tokens in the long run when comparing the players to each other.\n// </commentary>\n// </example>\n// \n// <example>\n// User: "Analyze a single large code repository for security vulnerabilities and generate a report."\n// Assistant: *Launches a single `task` subagent for the repository analysis*\n// Assistant: *Receives report and integrates results into final summary*\n// <commentary>\n// Subagent is used to isolate a large, context-heavy task, even though there is only one. This prevents the main thread from being overloaded with details.\n// If the user then asks followup questions, we have a concise report to reference instead of the entire history of analysis and tool calls, which is good and saves us time and money.\n// </commentary>\n// </example>\n// \n// <example>\n// User: "Schedule two meetings for me and prepare agendas for each."\n// Assistant: *Calls the task tool in parallel to launch two `task` subagents (one per meeting) to prepare agendas*\n// Assistant: *Returns final schedules and agendas*\n// <commentary>\n// Tasks are simple individually, but subagents help silo agenda preparation.\n// Each subagent only needs to worry about the agenda for one meeting.\n// </commentary>\n// </example>\n// \n// <example>\n// User: "I want to order a pizza from Dominos, order a burger from McDonald\'s, and order a salad from Subway."\n// Assistant: *Calls tools directly in parallel to order a pizza from Dominos, a burger from McDonald\'s, and a salad from Subway*\n// <commentary>\n// The assistant did not use the task tool because the objective is super simple and clear and only requires a few trivial tool calls.\n// It is better to just complete the task directly and NOT use the `task` tool.\n// </commentary>\n// </example>\n// \n// ### Example usage with custom agents:\n// \n// <example_agent_descriptions>\n// "content-reviewer": use this agent after you are done creating significant content or documents\n// "greeting-responder": use this agent when to respond to user greetings with a friendly joke\n// "research-analyst": use this agent to conduct thorough research on complex topics\n// </example_agent_descriptions>\n// \n// <example>\n// user: "Please write a function that checks if a number is prime"\n// assistant: Sure let me write a function that checks if a number is prime\n// assistant: First let me use the Write tool to write a function that checks if a number is prime\n// assistant: I\'m going to use the Write tool to write the following code:\n// <code>\n// function isPrime(n) {\n//   if (n <= 1) return false\n//   for (let i = 2; i * i <= n; i++) {\n//     if (n % i === 0) return false\n//   }\n//   return true\n// }\n// </code>\n// <commentary>\n// Since significant content was created and the task was completed, now use the content-reviewer agent to review the work\n// </commentary>\n// assistant: Now let me use the content-reviewer agent to review the code\n// assistant: Uses the Task tool to launch with the content-reviewer agent\n// </example>\n// \n// <example>\n// user: "Can you help me research the environmental impact of different renewable energy sources and create a comprehensive report?"\n// <commentary>\n// This is a complex research task that would benefit from using the research-analyst agent to conduct thorough analysis\n// </commentary>\n// assistant: I\'ll help you research the environmental impact of renewable energy sources. Let me use the research-analyst agent to conduct comprehensive research on this topic.\n// assistant: Uses the Task tool to launch with the research-analyst agent, providing detailed instructions about what research to conduct and what format the report should take\n// </example>\n// \n// <example>\n// user: "Hello"\n// <commentary>\n// Since the user is greeting, use the greeting-responder agent to respond with a friendly joke\n// </commentary>\n// assistant: "I\'m going to use the Task tool to launch with the greeting-responder agent"\n// </example>\ntype task = (_: {\n// A detailed description of the task for the subagent to perform autonomously. Include all necessary context and specify the expected output format.\ndescription: string,\n// The type of subagent to use. Must be one of the available agent types listed in the tool description.\nsubagent_type: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>developer<|message|># Instructions\n\nAuthenticated customer -> name: John Doe, email: john.doe@example.com.<|end|><|start|>user<|message|>I want a refund<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:05:56 Generated response chatcmpl-954811fd211827c0: output: '[tool_calls: task({"description": "Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.", "subagent_type": "refund_agent"})]', output_token_ids: '<|channel|>analysis<|message|>User says "I want a refund." We must delegate to refund_agent. As per instructions: "If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself." So we call the task tool, subagent_type: refund_agent, description: basically that.<|end|><|start|>assistant<|channel|>commentary to=functions.task <|constrain|>json<|message|>{"description":"Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.","subagent_type":"refund_agent"}<|call|>', finish_reason: tool_calls

DEBUG 12:05:56 Request chatcmpl-b7f719eec91c8a93 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:05:57 Generated response chatcmpl-b7f719eec91c8a93: output: '[tool_calls: search_orders({})]', output_token_ids: "<|channel|>analysis<|message|>The user wants us to handle the refund for the authenticated customer John Doe. We need to follow the flow: call search_orders first. Then if multiple orders, ask customer which one. Then match answer to one of orders. If unclear, ask again. Then call refund_eligibility. Then respond accordingly.\n\nWe need to call search_orders (no arguments) and get the returned orders. Then respond? However, we need to do interactive steps. We'll call search_orders. Let's do that.<|end|><|start|>assistant<|channel|>commentary to=functions.search_orders <|constrain|>json<|message|>{}<|call|>", finish_reason: tool_calls

DEBUG 12:05:57 Request chatcmpl-8fd5565575485dd2 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:05:59 Generated response chatcmpl-8fd5565575485dd2: output: '[tool_calls: ask_customer<|channel|>commentary({"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: '<|channel|>analysis<|message|>We need to ask customer if they\'ve multiple orders. There are two orders. According to flow, we must ask them to pick which one to refund. So ask question listing each option with item, color, amount, date. Ensure answer identifies specific order.\n\nWe\'ll ask: "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"<|end|><|start|>assistant<|channel|>commentary to=functions.ask_customer<|channel|>commentary <|constrain|>json<|message|>{"question":"Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|>', finish_reason: tool_calls

DEBUG 12:05:59 Request chatcmpl-9c43ae7feb521ed4 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:01 Generated response chatcmpl-9c43ae7feb521ed4: output: '[tool_calls: ask_customer({"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: '<|channel|>analysis<|message|>We attempted to use ask_customer incorrectly? Actually we used ask_customer within \'functions.to=functions.ask_customer...\'? The naming: we need to call ask_customer function. That\'s like {question: string}. So should be:\n\n```json\n{\n"question": "..."\n}\n```\n\nBut we also need to specify tool name. In previous code we used "functions.ask_customer" okay. The error says "ask_customer<|channel|>commentary is not a valid tool." That\'s because we incorrectly appended "<|channel|>commentary". We should simply call the function. We\'ll call directly: to=functions.ask_customer.\n\nThus:<|end|><|start|>assistant<|channel|>commentary to=functions.ask_customer<|constrain|>json<|message|>{"question":"You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|>', finish_reason: tool_calls

DEBUG 12:06:08 Request chatcmpl-bfccc4239e937865 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The one with the monkey picture<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:10 Generated response chatcmpl-bfccc4239e937865: output: '[tool_calls: ask_customer<|channel|>commentary({"question": "I\\u2019m sorry, I don\\u2019t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: '<|channel|>analysis<|message|>The customer responded: "The one with the monkey picture." However, orders we have: red shirt, blue shirt. None mention a monkey picture. So no match. We need to clarify with ask_customer again, show options. Ensure we match exactly one order. Response ambiguous. So we call ask_customer again listing options.<|end|><|start|>assistant<|channel|>commentary to=functions.ask_customer<|channel|>commentary<|constrain|>json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|>', finish_reason: tool_calls

DEBUG 12:06:10 Request chatcmpl-816b2267f0ee7489 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The one with the monkey picture<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:12 Generated response chatcmpl-816b2267f0ee7489: output: '[tool_calls: ask_customer<|channel|>commentary({"question": "I\\u2019m sorry, I don\\u2019t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: '<|channel|>analysis<|message|>The system: We must ask using ask_customer again. The last ask_customer call not relevant. We got response: "The one with the monkey picture". No match. Should ask again with options.\n\nWe need to follow flow: ask_customer again until clear match. I\'ll ask again. Then after match, check eligibility.\n\nSo we must call ask_customer: "The orders are ... Which would you like to refund?" Let\'s do again.<|end|><|start|>assistant<|channel|>commentary to=functions.ask_customer<|channel|>commentary<|constrain|>json<|message|>{"question":"I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|>', finish_reason: tool_calls

DEBUG 12:06:12 Request chatcmpl-ab8440d5fcd521c1 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The one with the monkey picture<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:15 Generated response chatcmpl-ab8440d5fcd521c1: output: '[tool_calls: ask_customer({"question": "I\\u2019m sorry, I don\\u2019t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"})]', output_token_ids: '<|channel|>analysis<|message|>We got a loop of ask_customer calls due to misunderstanding of syntax. Actually need to call ask_customer and then respond with the customer\'s answer. The system simulated but we are just ChatGPT; we need to ask question, but we will get no answer. In this task, we are to simulate interaction with user. The user didn\'t respond; we need to ask. Then after we get customer\'s answer, we follow.\n\nWe need to wait for the customer to reply. I think we can assume that in simulation, the user will respond in the next message. Right now we asked but we got the user’s answer: "The one with the monkey picture". That was from the error output? Wait: The assistant\'s own call had error "ask_customer is not a valid tool" but we saw the user\'s answer following? The log shows the user gave "The one with the monkey picture". That seems like a response to our last question. So we have an answer, but we need to match to the orders. "The one with the monkey picture" doesn\'t match either. We need to clarify again. Let\'s do that.<|end|><|start|>assistant<|channel|>commentary to=functions.ask_customer<|constrain|>json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|>', finish_reason: tool_calls

DEBUG 12:06:27 Request chatcmpl-9cf5290a4efbb34c details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The one with the monkey picture<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The red one<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:28 Generated response chatcmpl-9cf5290a4efbb34c: output: '[tool_calls: refund_eligibility<|channel|>commentary({"order_id": "order_001"})]', output_token_ids: '<|channel|>analysis<|message|>We got the answer: "The red one". We need to match to order: order_001 is red shirt. So that\'s order_id order_001. Then check refund eligibility.<|end|><|start|>assistant<|channel|>commentary to=functions.refund_eligibility<|channel|>commentary<|constrain|>json<|message|>{"order_id":"order_001"}<|call|>', finish_reason: tool_calls

DEBUG 12:06:28 Request chatcmpl-9b19bc0b1f141601 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The one with the monkey picture<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The red one<|end|><|start|>assistant to=functions.refund_eligibility<|channel|>commentary<|channel|>commentary json<|message|>{"order_id": "order_001"}<|call|><|start|>functions.refund_eligibility<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: refund_eligibility<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:29 Generated response chatcmpl-9b19bc0b1f141601: output: '[tool_calls: refund_eligibility({"order_id": "order_001"})]', output_token_ids: '<|channel|>analysis<|message|>We made mistakes: didn\'t pass arguments correctly? We need to call functions.refund_eligibility with parameters. My call syntax: to=functions.refund_eligibility\n\nLet\'s correct. Also previous ask_customer multiple calls. The customer answered "the red one". We need to match exactly to order_001. That is clear. Now call refund_eligibility with order_id: order_001. But we had error calling. We need to do to=functions.refund_eligibility without <commentary>. Let\'s redo.<|end|><|start|>assistant<|channel|>commentary to=functions.refund_eligibility<|constrain|>json<|message|>{"order_id":"order_001"}<|call|>', finish_reason: tool_calls

DEBUG 12:06:29 Request chatcmpl-b95b4be03f7ab272 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are refund_agent. You handle a customer\'s refund request from start to finish.\n\nYou have these tools:\n- search_orders(): look up every order the authenticated customer placed.\n- refund_eligibility(order_id): check if a specific order can be refunded (placed less than 7 days ago and under $150).\n- ask_customer(question): ask the customer a question and wait for their answer.\n\nFollow this flow:\n1. Call search_orders() to retrieve the customer\'s orders.\n2. If more than one order is returned, do NOT guess. Call ask_customer with a question that lists each option (item, color, amount and date) and asks which one to refund.\n3. Match the customer\'s answer to exactly one of the orders returned by search_orders(). The answer can be free text (a number, an order_id, or a description like "the blue one"). If the answer does NOT clearly match any of those orders, do NOT pick one anyway: call ask_customer again to clarify, showing the available options. Only continue once the answer identifies a specific order.\n4. Using the matched order, call refund_eligibility with that order\'s order_id.\n5. If it is eligible, tell the customer the refund was processed successfully. If it is not eligible, explain why (too old or too expensive).\n\nAlways base your decisions on the tool outputs, never on assumptions. Never refund an order the customer did not actually choose.\n\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Search all orders placed by the authenticated customer.\n// \n//     Returns the customer\'s orders (order_id, item, color, amount, date). The customer is\n//     taken from the authenticated session, not from any argument.\ntype search_orders = (_: {\n}) => any;\n\n// Check whether one of the customer\'s orders is eligible for a refund.\n// \n//     Given an order_id (from search_orders), returns whether it is eligible. An order is\n//     eligible only when placed less than 7 days ago and under $150.\ntype refund_eligibility = (_: {\norder_id: string,\n}) => any;\n\n// Ask the customer a clarifying question and wait for their answer.\n// \n//     Use this when you cannot proceed without input from the customer (for example, to\n//     choose which of several orders to refund). Execution pauses until the answer arrives.\ntype ask_customer = (_: {\nquestion: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>user<|message|>Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.<|end|><|start|>assistant to=functions.search_orders<|channel|>commentary json<|message|>{}<|call|><|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The one with the monkey picture<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: ask_customer<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.ask_customer<|channel|>commentary json<|message|>{"question": "I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|><|start|>functions.ask_customer to=assistant<|channel|>commentary<|message|>The red one<|end|><|start|>assistant to=functions.refund_eligibility<|channel|>commentary<|channel|>commentary json<|message|>{"order_id": "order_001"}<|call|><|start|>functions.refund_eligibility<|channel|>commentary to=assistant<|channel|>commentary<|message|>Error: refund_eligibility<|channel|>commentary is not a valid tool, try one of [ls, read_file, write_file, edit_file, glob, grep, execute, search_orders, refund_eligibility, ask_customer].<|end|><|start|>assistant to=functions.refund_eligibility<|channel|>commentary json<|message|>{"order_id": "order_001"}<|call|><|start|>functions.refund_eligibility to=assistant<|channel|>commentary<|message|>{"order_id": "order_001", "eligible": true}<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:30 Generated response chatcmpl-b95b4be03f7ab272: output: 'Your red shirt (order_001) is eligible for a refund. The refund has been processed successfully. If you need anything else, just let me know!', output_token_ids: '<|channel|>analysis<|message|>Now respond.<|end|><|start|>assistant<|channel|>final<|message|>Your red shirt (order_001) is eligible for a refund. The refund has been processed successfully. If you need anything else, just let me know!<|return|>', finish_reason: stop

DEBUG 12:06:30 Request chatcmpl-b14651add9f0ff42 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2026-08-01\n\nReasoning: medium\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.\nCalls to these tools must go to the commentary channel: \'functions\'.<|end|><|start|>developer<|message|># Instructions\n\nYou are customer_support_agent, the top-level customer support assistant.\n\nYour job is to understand what the customer wants:\n- If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself.\n- For anything that is not a refund, respond helpfully and briefly.\n\nThe authenticated customer\'s name and email are provided in the conversation context.\n\n\nYou are a deep agent, an AI assistant that helps users accomplish tasks using tools. You respond with text and tool calls. The user can see your responses and tool outputs in real time.\n\n## Core Behavior\n\n- Be concise and direct. Don\'t over-explain unless asked.\n- NEVER add unnecessary preamble ("Sure!", "Great question!", "I\'ll now...").\n- Don\'t say "I\'ll now do X" — just do it.\n- If the request is underspecified, ask only the minimum followup needed to take the next useful action.\n- If asked how to approach something, explain first, then act.\n\n## Professional Objectivity\n\n- Prioritize accuracy over validating the user\'s beliefs\n- Disagree respectfully when the user is incorrect\n- Avoid unnecessary superlatives, praise, or emotional validation\n\n## Doing Tasks\n\nWhen the user asks you to do something:\n\n1. **Understand first** — read relevant files, check existing patterns. Quick but thorough — gather enough evidence to start, then iterate.\n2. **Act** — implement the solution. Work quickly but accurately.\n3. **Verify** — check your work against what was asked, not against your own output. Your first attempt is rarely correct — iterate.\n\nKeep working until the task is fully complete. Don\'t stop partway and explain what you would do — just do it. Only yield back to the user when the task is done or you\'re genuinely blocked.\n\n**When things go wrong:**\n\n- If something fails repeatedly, stop and analyze *why* — don\'t keep retrying the same approach.\n- If you\'re blocked, tell the user what\'s wrong and ask for guidance.\n\n## Clarifying Requests\n\n- Do not ask for details the user already supplied.\n- Use reasonable defaults when the request clearly implies them.\n- Prioritize missing semantics like content, delivery, detail level, or alert criteria.\n- Avoid opening with a long explanation of tool, scheduling, or integration limitations when a concise blocking followup question would move the task forward.\n- Ask domain-defining questions before implementation questions.\n- For monitoring or alerting requests, ask what signals, thresholds, or conditions should trigger an alert.\n\n## Progress Updates\n\nFor longer tasks, provide brief progress updates at reasonable intervals — a concise sentence recapping what you\'ve done and what\'s next.\n\n## Following Conventions\n\n- Read files before editing — understand existing content before making changes\n- Mimic existing style, naming conventions, and patterns\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /. Follow the tool docs for the available tools, and use pagination (offset/limit) when reading large files.\n\n- ls: list files in a directory (requires absolute path)\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n- glob: find files matching a pattern (e.g., "**/*.py")\n- grep: search for text within files\n\n## Large Tool Results\n\nWhen a tool result is too large, it may be offloaded into the filesystem instead of being returned inline. In those cases, use `read_file` to inspect the saved result in chunks, or use `grep` within `/large_tool_results/` if you need to search across offloaded tool results and do not know the exact file path. Offloaded tool results are stored under `/large_tool_results/<tool_call_id>`.\n\n## `task` (subagent spawner)\n\nYou have access to a `task` tool to launch short-lived subagents that handle isolated tasks. These agents are ephemeral — they live only for the duration of the task and return a single result.\n\nWhen to use the task tool:\n\n- When a task is complex and multi-step, and can be fully delegated in isolation\n- When a task is independent of other tasks and can run in parallel\n- When a task requires focused reasoning or heavy token/context usage that would bloat the orchestrator thread\n- When sandboxing improves reliability (e.g. code execution, structured searches, data formatting)\n- When you only care about the output of the subagent, and not the intermediate steps (ex. performing a lot of research and then returned a synthesized report, performing a series of computations or lookups to achieve a concise, relevant answer.)\n\nSubagent lifecycle:\n\n1. **Spawn** → Provide clear role, instructions, and expected output\n2. **Run** → The subagent completes the task autonomously\n3. **Return** → The subagent provides a single structured result\n4. **Reconcile** → Incorporate or synthesize the result into the main thread\n\nWhen NOT to use the task tool:\n\n- If you need to see the intermediate reasoning or steps after the subagent has completed (the task tool hides them)\n- If the task is trivial (a few tool calls or simple lookup)\n- If delegating does not reduce token usage, complexity, or context switching\n- If splitting would add latency without benefit\n\n## Important Task Tool Usage Notes to Remember\n\n- Whenever possible, parallelize the work that you do. This is true for both tool_calls, and for tasks. Whenever you have independent steps to complete - make tool_calls, or kick off tasks (subagents) in parallel to accomplish them faster. This saves time for the user, which is incredibly important.\n- Remember to use the `task` tool to silo independent tasks within a multi-part objective.\n- You should use the `task` tool whenever you have a complex task that will take multiple steps, and is independent from other tasks that the agent needs to complete. These agents are highly competent and efficient.\n\nAvailable subagent types:\n\n- refund_agent: Handles the end-to-end refund request flow: finds the customer\'s orders, clarifies which order to refund, checks eligibility and confirms the refund.\n\n# Tools\n\n## functions\n\nnamespace functions {\n\n// Launch an ephemeral subagent to handle complex, multi-step independent tasks with isolated context windows.\n// \n// Available agent types and the tools they have access to:\n// - refund_agent: Handles the end-to-end refund request flow: finds the customer\'s orders, clarifies which order to refund, checks eligibility and confirms the refund.\n// \n// When using the Task tool, you must specify a subagent_type parameter to select which agent type to use.\n// \n// ## Usage notes:\n// 1. Launch multiple agents concurrently whenever possible, to maximize performance; to do that, use a single message with multiple tool uses\n// 2. When the agent is done, it will return a single message back to you. The result returned by the agent is not visible to the user. To show the user the result, you should send a text message back to the user with a concise summary of the result.\n// 3. Each agent invocation is stateless. You will not be able to send additional messages to the agent, nor will the agent be able to communicate with you outside of its final report. Therefore, your prompt should contain a highly detailed task description for the agent to perform autonomously and you should specify exactly what information the agent should return back to you in its final and only message to you.\n// 4. The agent\'s outputs should generally be trusted\n// 5. Clearly tell the agent whether you expect it to create content, perform analysis, or just do research (search, file reads, web fetches, etc.), since it is not aware of the user\'s intent\n// 6. If the agent description mentions that it should be used proactively, then you should try your best to use it without the user having to ask for it first. Use your judgement.\n// 7. When only the general-purpose agent is provided, you should use it for all tasks. It is great for isolating context and token usage, and completing specific, complex tasks, as it has all the same capabilities as the main agent.\n// \n// ### Example usage of the general-purpose agent:\n// \n// <example_agent_descriptions>\n// "general-purpose": use this agent for general purpose tasks, it has access to all tools as the main agent.\n// </example_agent_descriptions>\n// \n// <example>\n// User: "I want to conduct research on the accomplishments of Lebron James, Michael Jordan, and Kobe Bryant, and then compare them."\n// Assistant: *Uses the task tool in parallel to conduct isolated research on each of the three players*\n// Assistant: *Synthesizes the results of the three isolated research tasks and responds to the User*\n// <commentary>\n// Research is a complex, multi-step task in it of itself.\n// The research of each individual player is not dependent on the research of the other players.\n// The assistant uses the task tool to break down the complex objective into three isolated tasks.\n// Each research task only needs to worry about context and tokens about one player, then returns synthesized information about each player as the Tool Result.\n// This means each research task can dive deep and spend tokens and context deeply researching each player, but the final result is synthesized information, and saves us tokens in the long run when comparing the players to each other.\n// </commentary>\n// </example>\n// \n// <example>\n// User: "Analyze a single large code repository for security vulnerabilities and generate a report."\n// Assistant: *Launches a single `task` subagent for the repository analysis*\n// Assistant: *Receives report and integrates results into final summary*\n// <commentary>\n// Subagent is used to isolate a large, context-heavy task, even though there is only one. This prevents the main thread from being overloaded with details.\n// If the user then asks followup questions, we have a concise report to reference instead of the entire history of analysis and tool calls, which is good and saves us time and money.\n// </commentary>\n// </example>\n// \n// <example>\n// User: "Schedule two meetings for me and prepare agendas for each."\n// Assistant: *Calls the task tool in parallel to launch two `task` subagents (one per meeting) to prepare agendas*\n// Assistant: *Returns final schedules and agendas*\n// <commentary>\n// Tasks are simple individually, but subagents help silo agenda preparation.\n// Each subagent only needs to worry about the agenda for one meeting.\n// </commentary>\n// </example>\n// \n// <example>\n// User: "I want to order a pizza from Dominos, order a burger from McDonald\'s, and order a salad from Subway."\n// Assistant: *Calls tools directly in parallel to order a pizza from Dominos, a burger from McDonald\'s, and a salad from Subway*\n// <commentary>\n// The assistant did not use the task tool because the objective is super simple and clear and only requires a few trivial tool calls.\n// It is better to just complete the task directly and NOT use the `task` tool.\n// </commentary>\n// </example>\n// \n// ### Example usage with custom agents:\n// \n// <example_agent_descriptions>\n// "content-reviewer": use this agent after you are done creating significant content or documents\n// "greeting-responder": use this agent when to respond to user greetings with a friendly joke\n// "research-analyst": use this agent to conduct thorough research on complex topics\n// </example_agent_descriptions>\n// \n// <example>\n// user: "Please write a function that checks if a number is prime"\n// assistant: Sure let me write a function that checks if a number is prime\n// assistant: First let me use the Write tool to write a function that checks if a number is prime\n// assistant: I\'m going to use the Write tool to write the following code:\n// <code>\n// function isPrime(n) {\n//   if (n <= 1) return false\n//   for (let i = 2; i * i <= n; i++) {\n//     if (n % i === 0) return false\n//   }\n//   return true\n// }\n// </code>\n// <commentary>\n// Since significant content was created and the task was completed, now use the content-reviewer agent to review the work\n// </commentary>\n// assistant: Now let me use the content-reviewer agent to review the code\n// assistant: Uses the Task tool to launch with the content-reviewer agent\n// </example>\n// \n// <example>\n// user: "Can you help me research the environmental impact of different renewable energy sources and create a comprehensive report?"\n// <commentary>\n// This is a complex research task that would benefit from using the research-analyst agent to conduct thorough analysis\n// </commentary>\n// assistant: I\'ll help you research the environmental impact of renewable energy sources. Let me use the research-analyst agent to conduct comprehensive research on this topic.\n// assistant: Uses the Task tool to launch with the research-analyst agent, providing detailed instructions about what research to conduct and what format the report should take\n// </example>\n// \n// <example>\n// user: "Hello"\n// <commentary>\n// Since the user is greeting, use the greeting-responder agent to respond with a friendly joke\n// </commentary>\n// assistant: "I\'m going to use the Task tool to launch with the greeting-responder agent"\n// </example>\ntype task = (_: {\n// A detailed description of the task for the subagent to perform autonomously. Include all necessary context and specify the expected output format.\ndescription: string,\n// The type of subagent to use. Must be one of the available agent types listed in the tool description.\nsubagent_type: string,\n}) => any;\n\n} // namespace functions<|end|><|start|>developer<|message|># Instructions\n\nAuthenticated customer -> name: John Doe, email: john.doe@example.com.<|end|><|start|>user<|message|>I want a refund<|end|><|start|>assistant to=functions.task<|channel|>commentary json<|message|>{"description": "Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.", "subagent_type": "refund_agent"}<|call|><|start|>functions.task to=assistant<|channel|>commentary<|message|>Your red shirt (order_001) is eligible for a refund. The refund has been processed successfully. If you need anything else, just let me know!<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 12:06:32 Generated response chatcmpl-b14651add9f0ff42: output: 'Your refund has been processed successfully. If you have any other questions, let me know.', output_token_ids: '<|channel|>analysis<|message|>The refund_agent returned a message that the refund has been processed. But we are instructed not to process refunds ourselves. We should not say that we processed it. The refund_agent executed and apparently processed. But as per instruction: If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself. So we just tell the user that refund is being processed? The refund_agent presumably handled it. The subagent returned a message. We should communicate that the refund has been processed successfully? Actually the instructions say: do not process refunds yourself. We called the subagent. It processed. So we can relay result. The user just said "I want a refund". We can respond: Done. Thanks. Keep concise. So final answer: Confirm refund processed.<|end|><|start|>assistant<|channel|>final<|message|>Your refund has been processed successfully. If you have any other questions, let me know.<|return|>', finish_reason: stop

Observations

I’ll assume you didn’t read into the relatively large amount of texts that were generated, so I’ll map out the internal inference flows that were triggered by the happy path.

Delegation to the sub agent

The first inference is describing the customer_support_agent with the system prompt that I’ve provided, on top of that, deepagents attaching general rules of how the agents should behave, how it should handle task progress, when and how it should spawn sub agents and what output it should expect out of it. The only sub agent that is provided to this agent is the refund_agent. After all these definitions, we’ll see the initial request and it’s context wrapper within the model’s special tokens on which it was post-trained:

Authenticated customer -> name: John Doe, email: john.doe@example.com.<|end|><|start|>user<|message|>I want a refund<|end|>

If we compare the number of tokens in the first request that originated from our system prompt with the number added by the framework, we can see that our system prompt accounted for only 159 tokens, or approximately 4.9% of the total. One could argue that this large portion of the prompt can be customized through the middleware stack, and that would be correct. However, the effect would be limited because the tool usage instructions still account for a significant portion of the system prompt.

The response for this first inference hit is the instruction to delegate. It outputs a tool_calls to the sub agent:

[
    tool_calls:
    task({"description": "Handle refund for authenticated customer John Doe, email john.doe@example.com. 
    Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.",
    "subagent_type": "refund_agent"})
]

If we’ll examine the attached token output, we can see the reasoning for the response:
<|channel|>analysis<|message|>User says "I want a refund." We must delegate to refund_agent. As per instructions: "If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself." So we call the task tool, subagent_type: refund_agent, description: basically that.

How can the output be different than the output_token_ids?

I’ll briefly touch this point so things will be clear, but we will not deep dive into it.
The output_token_ids are the raw response from the model, while the output is the formatted response from the inference provider, in our case vLLM.
We’re using GPT-OSS model in this example, which works with OpenAI Harmony, basically leveraging the special tokens to transform the model response into a structured API output.

So the flows is:

Provider receives output tokens from the model:

[200005, 35644, 200008, 1844, 5003, 392, 40, 1682, 261, 18376, 3692, 1416, 2804, 28801, 316, 18376, 62035, 13, 1877, 777, 15543, 25, 392, 3335, 290, 5989, 382, 16054, 395, 261, 18376, 11, 28801, 290, 6062, 18376, 17491, 316, 290, 18376, 62035, 1543, 16932, 13, 3756, 625, 2273, 89728, 6675, 3692, 2632, 581, 2421, 290, 5296, 4584, 11, 1543, 16932, 3804, 25, 18376, 62035, 11, 6496, 25, 21924, 484, 13, 200007, 200006, 173781, 200005, 12606, 815, 316, 28, 44580, 27230, 220, 200003, 4108, 200008, 10848, 9186, 7534, 9654, 18376, 395, 76990, 5989, 5928, 78200, 11, 3719, 64626, 1380, 5578, 81309, 1136, 13, 9764, 122862, 12528, 11, 64461, 1118, 2569, 316, 18376, 11, 2371, 57418, 326, 7620, 18376, 85388, 3834, 16932, 3804, 7534, 148482, 62035, 18583, 200012]

Tokens translated to the following text:

<|channel|>analysis<|message|>User says "I want a refund." We must delegate to refund_agent. As per instructions: "If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself." So we call the task tool, subagent_type: refund_agent, description: basically that.<|end|><|start|>assistant<|channel|>commentary to=functions.task <|constrain|>json<|message|>{"description":"Handle refund for authenticated customer John Doe, email john.doe@example.com. Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.","subagent_type":"refund_agent"}<|call|>

vLLM user OpenAI Harmony to translate special tokens to structured tool call:

[
    tool_calls:
    task({"description": "Handle refund for authenticated customer John Doe, email john.doe@example.com. 
    Find customer\'s orders, clarify which order to refund, check eligibility and confirm refund.",
    "subagent_type": "refund_agent"})
]

The flow continues based on the structured output.

Now that we understand the difference between the output tokens and output itself, lets continue to analyze the internal inference flows happened during the execution of the happy path.

Sub agent - refund agent enters the flow

The second inference flow was triggered by the output of the first one, which explicitly requested the refund_agent. The ratio between our system prompt and the boilerplate system prompt is slightly better in this case because this agent does not have any subagent capabilities that require additional usage instructions. Our system prompt accounts for 396 out of 1,211 input tokens, or approximately 32% of the total.

This flow response is a call to search_orders:

[tool_calls: search_orders({})]

This structured tool call is generated with the reasoning of:

<|channel|>analysis<|message|>The user wants us to handle the refund for the authenticated customer John Doe. We need to follow the flow: call search_orders first. Then if multiple orders, ask customer which one. Then match answer to one of orders. If unclear, ask again. Then call refund_eligibility. Then respond accordingly.
We need to call search_orders (no arguments) and get the returned orders. Then respond? However, we need to do interactive steps. We'll call search_orders. Let's do that.<|end|>

Followed by the special tokens (as explained above), which are transformed to a structured response:

<|start|>assistant<|channel|>commentary to=functions.search_orders <|constrain|>json<|message|>{}<|call|>

Sub agent - tool response as an input, KV Cache kicks in

At this point, the general system prompt supplied by deepagents, our system prompt, the tool call and now the results of the search_orders tool are supplied as input to the next inference call:

.... previous tokens
<|start|>functions.search_orders to=assistant<|channel|>commentary<|message|>[{"order_id": "order_001", "item": "red shirt", "color": "red", "amount": 100, "date": "2026-07-29"}, {"order_id": "order_002", "item": "blue shirt", "color": "blue", "amount": 180, "date": "2026-07-22"}]<|end|><|start|>assistant

It might seem like things are piling up, but due to the fact that most of the prefix is repeated, we get a pretty high KV Cache hit rate:

Avg generation throughput: 38.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.8%, Prefix cache hit rate: 80.9%

The response is a call to ask_customer tool with required clarification from the user:

[
tool_calls: 
    ask_customer<|channel|>commentary({
        "question": "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 
                 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"
    })
]

with the valid reason due to the fact that there is more than one order:

<|channel|>analysis<|message|>We need to ask customer if they\'ve multiple orders. There are two orders. According to flow, we must ask them to pick which one to refund. So ask question listing each option with item, color, amount, date. Ensure answer identifies specific order. 
 
We\'ll ask: "Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"<|end|><|start|>assistant<|channel|>commentary to=functions.ask_customer<|channel|>commentary <|constrain|>json<|message|>{"question":"Sure. You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?"}<|call|>

Hidden taxes cause 57% more inference requests

Did you notice something interesting in the flow? Our business logic dictates that we should see 7 inference requests:

1. 1 general customer support agent
2. 1 refund agent
3. 1 for search_orders tool call and its response
4. 2 ask customer tool call and response (first question and then clarification)
5. 1 refund_eligibility tool call and its response
6. 1 final confirmation to the user

But how come we see 11, 57% more than expected?

A closer examination of the logs shows us that internally there were errors that were automatically corrected by the deepagents harness.

# Happened 3 times
output: '[tool_calls: ask_customer<|channel|>commentary ....  '

# Happened 1 time
output: '[tool_calls: refund_eligibility<|channel|>commentary ....' 

The model responded with a wrong tool call, 3 times for the ask_customer question and 1 time for the refund_eligibility.
On the bright side, the harness automatically corrected the tool call and re-issued the inference request, but on the other side, it almost doubled the inference requests.
Now, one can argue that the model is small and old, and the errors shouldn’t occur every time, but a counterargument might be that the tool definitions are extremely straightforward. We can increase the model size, but with more complex functions the error rate will also increase, or we can keep simplified tool definitions with a better model, but we might be overpaying. Like I’ve written above, everything’s a tradeoff and decided on a per-case basis.
For now, we can accept that there were errors in the LLM response and that the harness recovered from them.

Continuing the flow

I think the idea of tool responses and the following reasoning is fairly clear at this point. I will not show each tool call and its reasoning, but basically the flow continues as described in the happy path:

1. User gives ambiguous answer and their response is appended to previous interactions and sent as input
2. Model understands that the answer is ambiguous and requests another ask_customer tool call
3. This time the user gives a proper answer (red shirt), everything is appended again and sent as input
4. Model understands the answer and calls refund_eligibility tool with the order_id of the red shirt
5. The tool returns that the order is eligible for a refund, everything is appended again and sent as input
6. Model understands that the order is eligible and confirms the refund to the user as a summary, and this time provides finish_reason: stop

Usage Summary

If we summarize the token usage, we’ll see that we used 18,302 input tokens and 1,730 output tokens, for a total of 20,032 tokens. Obviously not all of these tokens wasted FLOPs due to KV Cache hit rate, but since KV Cache is a separate and relatively major subject (cache eviction due to load, cache offloading to protect the GPU, external KV Cache sources), in this post let’s focus on the number of tokens and not the number of FLOPs that were required to generate these tokens.

Workflow / Orchestration Driven

Now let’s create a workflow / orchestration driven agent which can complete the same happy path.
Here also I will work with langchain, but same as stated in the multi-agent harness, it’s just a tool to demonstrate a concept. You can replace it with any other alternative such as Microsoft Agent Framework Workflows, CrewAI Flows or anything else.

We will reuse the mock APIs as well as the system prompt for the customer support agent from the multi-agent implementation, to make things as comparable as possible.

Customer Support Graph and Refund Graph

Very similarly to the multi-agent harness, we will create a graph for the customer support agent and a graph for the refund agent. Each graph will have nodes that control the flow. The added nodes will support the possible happy path that we’ve defined above. We’ll use with_structured_output to define the required output from the inference, and this output will be used to determine the next node in the graph.

Code: Customer Support Graph

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import END, START, StateGraph
from langgraph.types import Command, interrupt
from typing import Literal, TypedDict

def _build_customer_support_graph(checkpointer):
    builder = StateGraph(SupportState)
    builder.add_node("intent_detection", intent_detection)
    builder.add_edge(START, "intent_detection")
    builder.add_edge("intent_detection", END)
    return builder.compile(checkpointer=checkpointer)

def intent_detection(state: SupportState) -> dict:
    llm = get_llm().with_structured_output(IntentResult)
    messages = [
        {"role": "system", "content": CUSTOMER_SUPPORT_PROMPT}, # CUSTOMER_SUPPORT_PROMPT is the same system prompt we've used in the multi agent architecture
        {"role": "user", "content": state["request"]},
    ]
    result: IntentResult = llm.invoke(messages)
    return {"intent": result.intent}

One important thing to note about the refund graph is that a deterministic approach is used wherever possible.
The only points that truly require semantic understanding are intent generation and user clarification regarding which product they want refunded. This logic is handled by the _match_order function, which is used during both the clarification and initial product description nodes.
All other inputs and outputs are constructed using simple templates. One might argue that a simpler semantic matching approach could also have been used for the product description, but that would be beside the point I am trying to demonstrate.

Code: Refund Graph

def search_orders(state: RefundState) -> dict:
    orders = search_orders_by_user_id(state["user_id"])
    return {"orders": orders}


def route_after_search(state: RefundState) -> str:
    count = len(state["orders"])
    if count == 0:
        return "no_orders"
    if count > 1:
        return "clarify_selection"
    return "verify_refund"


def _match_order(orders: list, answer: str) -> str | None:
    llm = _LLM.with_structured_output(MatchResult)
    result: MatchResult = llm.invoke(build_match_check_prompt(orders, answer))
    valid_ids = {o["order_id"] for o in orders}
    return result.order_id if result.order_id in valid_ids else None


def clarify_selection(state: RefundState) -> dict:
    orders = state["orders"]
    prompt = build_selection_template(orders)

    while True:

        answer = str(interrupt(prompt))

        order_id = _match_order(orders, answer)
        if order_id is not None:
            return {"selected_order_id": order_id}

        prompt = build_no_match_template(orders, answer)


def verify_refund(state: RefundState) -> dict:
    order_id = state.get("selected_order_id")
    order = next((o for o in state["orders"] if o["order_id"] == order_id))
    return {"selected_order_id": order_id, "eligible": check_refund_eligibility(order)}


def route_after_verify(state: RefundState) -> str:
    return "process_refund" if state["eligible"] else "reject_refund"


def process_refund(state: RefundState) -> dict:
    return {"reply": REFUND_SUCCESS.format(order_id=state["selected_order_id"])}


def reject_refund(state: RefundState) -> dict:
    return {"reply": REFUND_REJECTED.format(order_id=state["selected_order_id"])}


def no_orders(state: RefundState) -> dict:
    return {"reply": NO_ORDERS_REPLY}


def _build_refund_graph(checkpointer):
    builder = StateGraph(RefundState)
    builder.add_node("search_purchases", search_orders)
    builder.add_node("clarify_selection", clarify_selection)
    builder.add_node("verify_refund", verify_refund)
    builder.add_node("process_refund", process_refund)
    builder.add_node("reject_refund", reject_refund)
    builder.add_node("no_orders", no_orders)

    builder.add_edge(START, "search_purchases")
    builder.add_conditional_edges(
        "search_purchases",
        route_after_search,
        {
            "clarify_selection": "clarify_selection",
            "verify_refund": "verify_refund",
            "no_orders": "no_orders",
        },
    )
    builder.add_edge("clarify_selection", "verify_refund")
    builder.add_conditional_edges(
        "verify_refund",
        route_after_verify,
        {"process_refund": "process_refund", "reject_refund": "reject_refund"},
    )
    builder.add_edge("process_refund", END)
    builder.add_edge("reject_refund", END)
    builder.add_edge("no_orders", END)
    return builder.compile(checkpointer=checkpointer)

Now let’s add the start/resume functions:

Code: Refund Graph

_CHECKPOINTER = InMemorySaver()
_SUPPORT_GRAPH = _build_customer_support_graph()
_REFUND_GRAPH = _build_refund_graph(_CHECKPOINTER)


_INTENT_GRAPHS = {"refund": _REFUND_GRAPH}


def start_flow(token: str, message: str, thread_id: str) -> dict | None:

    user = authorize_user_by_token(token)

    intent = _SUPPORT_GRAPH.invoke({"request": message})["intent"]

    graph = _INTENT_GRAPHS.get(intent)

    config = {"configurable": {"thread_id": thread_id}}

    result = graph.invoke({"user_id": user["user_id"]}, config=config)

    interrupts = result.get("__interrupt__")

    if interrupts:
        return {"status": INTERRUPTED, "prompt": interrupts[0].value, "thread_id": thread_id}

    return {"status": COMPLETED, "reply": result.get("reply", ""), "thread_id": thread_id}


def resume_flow(token: str, feedback: str, thread_id: str) -> dict | None:

    config = {"configurable": {"thread_id": thread_id}, "metadata": {"agent": REFUND_AGENT_TAG}}

    result = _REFUND_GRAPH.invoke(Command(resume=feedback), config=config)

    interrupts = result.get("__interrupt__")

    if interrupts:
        return {"status": INTERRUPTED, "prompt": interrupts[0].value, "thread_id": thread_id}

    return {"status": COMPLETED, "reply": result.get("reply", ""), "thread_id": thread_id}

Let’s run the happy path again

Like with deepagents, let’s run the same happy path.

python .\langgraph_app\run.py
 
Input: I want a refund
Output: You have two orders: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?
Input: The one with the monkey picture
Output: I’m sorry, I don’t see a monkey picture in your orders. The available options are: 1) Red shirt (red, $100, placed 2026-07-29), 2) Blue shirt (blue, $180, placed 2026-07-22). Which would you like to refund?
Input: The red one
Output: Your refund has been processed successfully. If you have any other questions, let me know.

Since I’ve already explained about decoding and showed a sample, in this flow let’s jump straight to the decoded inference logs and from there to observations.

Code: Decoded Logs

DEBUG 13:50:17 Request chatcmpl-a8fe94b0a1ab1302 details: prompt: None, prompt_token_ids: "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.Knowledge cutoff: 2024-06
Current date: 2026-08-04

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>developer<|message|># Instructions

You are customer_support_agent, the top-level customer support assistant.

Your job is to understand what the customer wants:
- If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent. Do not process refunds yourself.
- For anything that is not a refund, respond helpfully and briefly.

The authenticated customer's name and email are provided in the conversation context.
<|end|><|start|>user<|message|>I want a refund<|end|><|start|>assistant", prompt_embeds shape: None.

INFO 13:50:19 Generated response chatcmpl-a8fe94b0a1ab1302: output: '{"intent":"refund"}', output_token_ids: '<|channel|>analysis<|message|>We have a conversation. The user says "I want a refund". According to developer instructions: If the customer is asking for a refund, delegate whole refund handling to refund_agent subagent. Do not process refunds yourself. So we should produce a message that delegates to refund_agent. Provide proper response. The instructions say: "If the customer is asking for a refund, delegate the whole refund handling to the refund_agent subagent." So the assistant should presumably say something like "Sure, I\'ll forward your request to the refund specialist." but must delegate to refund_agent. We do not process directly. We just mention we are redirecting.

Thus the output: "Sure, I will forward your request to the refund team for you." or similar.<|end|><|start|>assistant<|channel|>final<|message|>{"intent":"refund"}<|return|>', finish_reason: stop

DEBUG 13:50:23 Request chatcmpl-b7deff35be847ac5 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2026-08-04

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>The customer was asked which purchase to refund. Given their answer and the list of orders below, return the matching order_id exactly as shown, or null if their answer matches none of them.

Orders:
- red shirt (red) $100 order_id=order_001
- blue shirt (blue) $180 order_id=order_002

Customer answer: The one with the monkey picture<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 13:50:23 Generated response chatcmpl-b7deff35be847ac5: output: '{"order_id": null}', output_token_ids: '<|channel|>analysis<|message|>We need to parse. There are orders: red shirt, blue shirt. Customer answer: "The one with the monkey picture". None of the orders mention monkey picture. So return null. Just output null.<|end|><|start|>assistant<|channel|>final<|message|>{"order_id": null}<|return|>', finish_reason: stop

DEBUG 13:50:29 Request chatcmpl-bb7dce083637ee83 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2026-08-04

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>The customer was asked which purchase to refund. Given their answer and the list of orders below, return the matching order_id exactly as shown, or null if their answer matches none of them.

Orders:
- red shirt (red) $100 order_id=order_001
- blue shirt (blue) $180 order_id=order_002

Customer answer: The one with the monkey picture<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 13:50:31 Generated response chatcmpl-bb7dce083637ee83: output: '{
  "order_id": null
}', output_token_ids: '<|channel|>analysis<|message|>We need to parse the question. "The customer was asked which purchase to refund. Given their answer and the list of orders below, return the matching order_id exactly as shown, or null if their answer matches none of them." So we have orders: red shirt (red) $100 order_id=order_001, blue shirt (blue) $180 order_id=order_002. The customer answer: "The one with the monkey picture". None of the orders mention a monkey picture. The orders mention colors and types of shirts; no monkey picture. So answer should be null. Return null exactly. So we just output null.<|end|><|start|>assistant<|channel|>final<|message|>{
  "order_id": null
}<|return|>', finish_reason: stop

DEBUG 13:50:31 Request chatcmpl-bbdade4ae0ffbc32 details: prompt: None, prompt_token_ids: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2026-08-04

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>The customer was asked which purchase to refund. Given their answer and the list of orders below, return the matching order_id exactly as shown, or null if their answer matches none of them.

Orders:
- red shirt (red) $100 order_id=order_001
- blue shirt (blue) $180 order_id=order_002

Customer answer: The red one<|end|><|start|>assistant', prompt_embeds shape: None.

INFO 13:50:32 Generated response chatcmpl-bbdade4ae0ffbc32: output: '{ "order_id": "order_001" }', output_token_ids: '<|channel|>analysis<|message|>We need to parse the question. The instruction: "The customer was asked which purchase to refund. Given their answer and the list of orders below, return the matching order_id exactly as shown, or null if their answer matches none of them."

Orders list with items: "red shirt (red) $100 order_id=order_001" and "blue shirt (blue) $180 order_id=order_002". Customer answer: "The red one".

Thus match red shirt. The matching order_id is order_001. So output "order_001". Clarify: return exactly as shown, no quotes? The instruction: return the matching order_id exactly as shown. Probably just the string. So answer: order_001.<|end|><|start|>assistant<|channel|>final<|message|>{ "order_id": "order_001" }<|return|>', finish_reason: stop

Observations

Significantly smaller system prompts and no mistake taxes

As you can see, even though the Harmony template is applied to the input, the system prompt contains much less boilerplate. Which makes sense - in this flow the model doesn’t need to understand which tools to call and when, only what kind of output is expected from it. On top of that, no matter how much I’ve tried, I couldn’t reproduce the error that occurred quite easily in the multi-agent harness and caused the erroneous tool calling tax. I will elaborate slightly more on the ability to use smaller models in more controlled flows later in this entry.

Usage Summary - reduced by a factor of 20

As mentioned above, inference calls were made only at the crossroads which required semantic understanding. Intent recognition when we pivoted from the general customer support graph, and the two attempts to match user description to the existing orders list.
This, combined with smaller prompts and no mistakes, completed the happy path with 592 input, 436 output and a total of 1028 tokens, ~20 times less than the multi-agent flow.

Token budget is only half the story

This is kind of a small paragraph relative to the rest of the post, but it’s every bit as important as the two architectures comparison. Obviously the example I’ve provided in this post is a simple one, but if we generalize to more complex use cases - making sure your harness is a good fit for the boundaries of the agent in your ecosystem does not only impact token budget, but also the required quality of the models you need to use and the error-recovery taxation.
In every agentic workflow there are deterministic and non-deterministic paths. Carefully curating the workflow to handle deterministic paths in a deterministic way will reduce the complexity of the workflow, thus reducing the need for stronger models.
Whether your workflow completes in 1M tokens of Fable 5 or 1M tokens of Haiku 4.5 is a 10X difference in cost.
On top of that, the only thing better than a strong, resilient harness with impeccable error recovery is a harness that rarely has any errors to recover from, since every error recovery in a non-deterministic path costs us.

Key Takeaway

I have presented a simplified, black-and-white example, but real-world systems are much more complex.
At what point do the development and maintenance costs of a carefully curated harness, including customer support and customer satisfaction considerations, outweigh the potential cost savings?

How much detail should the design include? Perhaps some parts of the workflow should use a multi-agent approach, with each agent carefully curated. Perhaps some agents should be tightly designed, while others can be allowed to operate more freely within the security boundaries of the ecosystem.

These are all multi-million-dollar questions that we should ask ourselves. The answers are highly dependent on the specific use case and often involve subjective tradeoffs.
Some organizations may lack enough experienced developers and therefore prefer not to invest deeply in harness design. Others may operate under strict budget or hardware constraints and therefore favor a lower-level, more carefully optimized approach. Critical workloads will generally push toward as many deterministic outcomes as possible.

Personally, given the products I currently work on, whenever I design a path an agent should take, I ask myself a simple question: Can this step be deterministic without limiting the next available steps in the agent path? If the answer is yes, the step will be deterministic. If the answer is no, it will be LLM-based.