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

1716

Output Tokens

286

Cost

$0.02

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 wants flights from London to Paris for tomorrow (2025-05-29). The calendar tool has already returned the date. Now I need to update the scratch pad to reflect that the flights tool is being called with the appropriate parameters.

Response: Current step: Calling 'flights' tool with parameters {from_city: 'London', to_city: 'Paris', date: '2025-05-29'}.
Remaining goals:
- Receive and interpret flight search results.
- Compile and present the available flights to the user.
Notes:
- Date '2025-05-29' confirmed from calendar tool output.
- Date format YYYY-MM-DD matches flights tool requirements.
- Await flights tool response and store results in working memory for final summary.
Progress: Step 3 of 4 — flights tool invocation in progress.