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.

Input Tokens

Output Tokens

Duration

Cost

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 me analyze the current situation and determine how to update the scratch pad:

1. Current state:
- We are in cycle 3
- Calendar tool has returned the date '2025-05-29'
- We have decided to use the flights tool next

2. What needs to be updated:
- Need to indicate we're calling the flights tool
- Should maintain remaining goals
- Should update progress indicator

3. The flights tool parameters are clear:
- from_city: London
- to_city: Paris
- date: 2025-05-29

Response: Current step: Calling flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'
Remaining goals:
- Get available flights from London to Paris for 2025-05-29
- Compile and present the results to the user
Notes:
- Flight search results will need to be processed in next step
- Prepare to format results for user presentation
Progress: Step 3 of 4