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

10s

Input Tokens

1149

Output Tokens

182

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 has explicitly requested to update the scratch pad. The current state is clear: we have the date (2025-05-29) from the calendar tool and the goal is to use the flights tool to find available flights from London to Paris for this date. The scratch pad should be updated to reflect the decision to call the flights tool with the confirmed parameters. I will update the scratch pad to reflect the next action and remove the calendar interpretation step as it's completed.

Response: Scratch pad updated:
Current step: Call flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'.
Remaining goals:
- Retrieve available flights from the flights tool.
- Present the results to the user.
Progress: Step 3 of 4.