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.

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.

Reasoning: 1) Understand the user's intent: The user asked for available flights from London to Paris for tomorrow. The calendar tool returned '2025-05-29' in the previous cycle, so that is the travel date to use. 2) Verify tool requirements: The 'flights' tool requires a date in YYYY-MM-DD format — '2025-05-29' matches this format. 3) Prepare the flights tool call: Set parameters from_city='London', to_city='Paris', date='2025-05-29'. 4) Decide next action: Call the 'flights' tool now with the prepared parameters. 5) Update the scratch pad to reflect the decision and next steps, advance progress from Step 2 to Step 3 of 4, and note what to do after the tool returns (interpret results, store in working memory, compile and present results). Response: Updated scratch_pad: Current step: Call 'flights' tool with prepared parameters and await results. Action to perform now: Invoke flights tool with: - from_city: "London" - to_city: "Paris" - date: "2025-05-29" Rationale/Checks: - Date obtained from calendar tool: '2025-05-29'. - Confirmed date format YYYY-MM-DD matches the flights tool schema. Planned post-tool steps: - On receiving flights tool output: interpret results, extract available flights, prices, times, and other relevant details. - Store the flight search result in working memory for summary and follow-up. - Compile and present the results to the user (clear listing and any suggestions). Remaining goals: - Use 'flights' tool to get available flights from London to Paris for 2025-05-29. - Compile and present the results to the user. Progress: Step 3 of 4. Next action (immediate): Call the 'flights' tool with the parameters above.

Agents

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Expected output

Model output