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

1248

Output Tokens

424

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's request is to find flights from London to Paris for tomorrow. The calendar tool previously confirmed tomorrow's date as 2025-05-29. The next step is to use the 'flights' tool with parameters from_city='London', to_city='Paris', date='2025-05-29'. This aligns with the remaining goals in the scratch pad. The date format matches the tool's requirement (YYYY-MM-DD). After retrieving flight data, results will be compiled for the user.

Response: Calling flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Updating scratch pad to reflect this action and pending flight data retrieval.