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

1143

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 user requested flights from London to Paris for tomorrow. The calendar tool was already used in cycle 2 to determine tomorrow's date as 2025-05-29. Following the plan, the next step is to call the flights tool with the confirmed date. The flights tool requires from_city, to_city, and date parameters. All required parameters are available: London (from_city), Paris (to_city), and 2025-05-29 (date). No additional tools are needed. The result will be stored in working memory for the final summary.

Response: Calling flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'