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

1149

Output Tokens

467

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 agent has determined tomorrow's date (2025-05-29) from the calendar tool result. Now the next logical step is to call the flights tool to find available flights from London to Paris for that date. I need to update the scratch pad to reflect this decision and track the current progress. This is cycle 3, and we're moving from interpreting the calendar result to calling the flights tool. The scratch pad should document the tool to be called with its parameters and update the progress.

Response: scratch_pad: Current step: Execute flights tool search with parameters (from_city: 'London', to_city: 'Paris', date: '2025-05-29').
Remaining goals:
- Wait for flights tool results.
- Compile and present available flights to the user.
Notes:
- Date format confirmed: YYYY-MM-DD (2025-05-29).
- Ready to proceed with flight search.
Progress: Step 3 of 4 (Initiating flight search).