Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

Duration

18s

Input Tokens

1628

Output Tokens

191

Cost

$0.00

Context

Input

You have decided to call the flight tool, update the scratch pad

Expected output

The scratch pad is updated with the new action, example model_output: Current step: Called 'flights' tool with parameters (from_city: London, to_city: Paris, date: 2025-05-29); waiting for tool result.
Remaining goals:
- Receive and process the flight search result.
- Summarize available flight options and present them to the user.
Notes:
- Ensure the result includes key details (flight numbers, times, prices if available).
- Store the raw flight result in working memory for reference.
- Prepare final summary once results are in.
Progress: Step 3 of 4.

Model output

Reasoning: My task is to call the flight tool with the correct parameters based on the context and update the scratch pad. The context provides the cities (London to Paris) and the date (2025-05-29) obtained from the calendar tool. I need to construct the correct tool call and update the scratch pad to reflect this action.

Response: ```tool_code
{"tool_name": "flights", "parameters": {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}}
```

```scratch_pad
Now calling the flights tool to find flights from London to Paris on 2025-05-29.
```