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

19s

Input Tokens

1263

Output Tokens

368

Cost

$0.01

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, we just received the date '2025-05-29' from the calendar tool. The user requested flights from London to Paris for tomorrow. The next logical step is to call the 'flights' tool with 'London' as the from_city, 'Paris' as the to_city, and '2025-05-29' as the date. The scratch pad needs to be updated to reflect this current step, update the remaining goals, and increment the progress.

Response: Current step: Call the 'flights' tool with from_city='London', to_city='Paris', and date='2025-05-29'.
Remaining goals:
- Compile and present the flight results to the user.
Notes:
- Store flight search result in working memory for summary.
Progress: Step 3 of 4.