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

1261

Output Tokens

215

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: I'll update the scratch pad based on the context and current task. The previous step involved getting the date from the calendar tool (2025-05-29), and the next logical step is to call the flights tool to search for available flights from London to Paris on that date.

Response: Updated scratch pad with the next planned action to use the 'flights' tool for finding available flights from London to Paris on 2025-05-29. The current goals remain:
- Use 'flights' tool to get available flights from London to Paris for 2025-05-29.
- Compile and present the results to the user.

Notes:
- Date format is confirmed to match tool requirements (YYYY-MM-DD).
- Preparing to store flight search result in working memory for summary.

Progress: Moving to Step 3 of 4 (Calling flights tool).