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

1090

Output Tokens

207

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 wants flights from London to Paris for tomorrow. The calendar tool has already returned '2025-05-29' as tomorrow's date. The next step is to call the flights tool with the required parameters: from_city='London', to_city='Paris', date='2025-05-29'. I need to update the scratch pad to reflect that the flights tool is now being invoked.

Response: Scratch pad updated: 
Current step: Invoke flights tool with the following parameters: from_city='London', to_city='Paris', date='2025-05-29'.
Remaining goals:
- Obtain and store the flight search result in working memory.
- Compile and present a concise summary of available flights to the user.
Notes:
- Parameters confirmed: from_city='London', to_city='Paris', date='2025-05-29'.
- Ready to process tool output.
Progress: Step 3 of 4.