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

11s

Input Tokens

1194

Output Tokens

580

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: I need to analyze the current situation and take the next logical step. The user wants to find flights from London to Paris for tomorrow, and the calendar tool has confirmed tomorrow's date is 2025-05-29. Looking at the available tools, I have a 'flights' tool that can find available flights. The tool requires three parameters: from_city, to_city, and date in YYYY-MM-DD format. I have all the required information: London (from_city), Paris (to_city), and 2025-05-29 (date). According to the scratch pad, this is step 2 of 4, and I need to use the 'flights' tool to get the available flights. After calling this tool, I'll need to update the scratch pad to reflect this action and prepare for interpreting the results in the next cycle.

Response: Calling flights tool with parameters: from_city="London", to_city="Paris", date="2025-05-29"