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.

100
Duration
15s
Input Tokens
1085
Output Tokens
250
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 context indicates that after the user's request for available flights from London to Paris for tomorrow, the calendar tool was used to determine tomorrow's date, which is '2025-05-29'. The next logical step is to use the flights tool to search for flights from London to Paris on that date. The instructions stress the importance of breaking tasks into steps, tracking progress, and updating the scratch pad accordingly. The scratch pad should reflect a new entry explaining the action of calling the flights tool with the appropriate parameters (from_city: London, to_city: Paris, date: 2025-05-29), as well as noting that the result should be stored for later summarization.

Response: Scratch pad updated:
Current step: Call 'flights' tool with parameters:
  - from_city: London
  - to_city: Paris
  - date: 2025-05-29
Action: Initiate 'flights' tool to retrieve available flights for the specified route and date. Once results are received, store them in working memory for subsequent summarization and user presentation.
Progress: Step 3 of 4.