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

Input Tokens

1085

Output Tokens

196

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: The task is to update the scratch pad after deciding to call the flight tool. The context provided indicates that the user needs information about available flights from London to Paris for tomorrow. The previous cycle determined tomorrow's date as '2025-05-29' using the calendar tool. The current cycle involves interpreting this result and deciding the next action, which is to use the 'flights' tool to get available flights for the specified date. The scratch pad needs to be updated to reflect the progress and the next steps.

Response: Scratch pad updated: Current step: Use 'flights' tool to get available flights from London to Paris for 2025-05-29. Remaining goals: Compile and present the results to the user. Notes: Confirm that the date format matches tool requirements. Store flight search result in working memory for summary. Progress: Step 3 of 4.