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
18s
Input Tokens
1085
Output Tokens
215
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 requested information about available flights from London to Paris for tomorrow. We have already determined tomorrow's date as 2025-05-29 using the calendar tool. The next logical step, as indicated in the scratch pad and remaining goals, is to call the 'flights' tool to get the available flights for this date. The flights tool requires the parameters 'from_city', 'to_city', and 'date'. I will prepare these parameters accordingly: 'from_city' as 'London', 'to_city' as 'Paris', and 'date' as '2025-05-29'. After calling the flights tool, I will update the scratch pad to record this action and await the flight data to proceed with summarizing and presenting results to the user.

Response: Call the flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Update the scratch pad to reflect this action and await the flights tool results.