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

1261

Output Tokens

237

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: Let's analyze the situation and update the scratch pad appropriately:
1. We are in cycle 3 of the process
2. The calendar tool has already been called and returned '2025-05-29'
3. Based on the current scratch pad and trajectory, we're about to call the flights tool
4. We need to reflect this next action in the scratch pad and maintain our progress tracking
5. The scratch pad should show we're moving from interpreting calendar results to executing the flight search

Response: Current step: Executing flight search with parameters (London → Paris, date: 2025-05-29)
Remaining goals:
- Process and analyze flight search results
- Compile and present the results to the user
Notes:
- Flight search parameters: from='London', to='Paris', date='2025-05-29'
- Store flight search result in working memory for summary
Progress: Step 3 of 4