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

29s

Input Tokens

1171

Output Tokens

1020

Cost

$0.01

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: The user request is to find flights from London to Paris for tomorrow. The calendar tool returned tomorrow's date as 2025-05-29. The next logical step is to call the flights tool with from_city='London', to_city='Paris', and date='2025-05-29' to retrieve available flights. After initiating the tool call, the scratch pad should be updated to reflect that the tool has been called and we are waiting for the result, moving the progress forward.

Response: Current step: Called 'flights' tool with from_city='London', to_city='Paris', date='2025-05-29'. Awaiting tool result.
Remaining goals:
- Receive flight search result from the tool.
- Compile and present the results to the user.
Notes:
- Date format matches tool requirements (YYYY-MM-DD).
- Store flight search result in working memory for summary.
Progress: Step 3 of 4.