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

24s

Input Tokens

1190

Output Tokens

574

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: Based on the context, I have determined tomorrow's date (2025-05-29) using the calendar tool. The next logical step is to call the flights tool with the required parameters: from_city='London', to_city='Paris', and date='2025-05-29'. I need to update the scratch pad to reflect this decision, change the current step, update remaining goals, and advance progress from Step 2 to Step 3.

Response: Current step: Call 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'.
Remaining goals:
- Wait for flights tool result
- Compile and present the flight information to the user
Notes:
- Flight search parameters confirmed: London → Paris on 2025-05-29
- Date format matches YYYY-MM-DD requirement
- Ready to execute tool call
Progress: Step 3 of 4