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

1114

Output Tokens

187

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 user requested information about available flights from London to Paris for tomorrow. In the previous cycle, the calendar tool was used to determine that tomorrow's date is '2025-05-29'. With this date now confirmed and in the correct YYYY-MM-DD format, the next logical step is to use the flights tool with the parameters: from_city='London', to_city='Paris', and date='2025-05-29'. After retrieving the flight data, I will compile and present the results to the user. I am now updating the scratch pad to reflect the current action and progress.

Response: I am now calling the flights tool to retrieve available flights from London to Paris for 2025-05-29. The scratch pad has been updated to reflect this action.