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

23s

Input Tokens

1084

Output Tokens

1189

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: Based on the user’s request and context, the next logical step is to invoke the flights tool using the date returned by the calendar tool (2025-05-29) to retrieve available flight options from London to Paris. We then update the scratch pad to indicate that we are proceeding with the flight lookup step. This maintains a clear record of our next action toward completing the user’s goal.

Response: Proceeding to call the flights tool with from_city='London', to_city='Paris', and date='2025-05-29'. Updating the scratch pad to reflect this next action—Step 3: Perform flight lookup using 'flights' tool.