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

1758

Output Tokens

169

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: As an assistant, my task is to help the user find information about available flights from London to Paris for the specified date. To accomplish this, I will use the 'flights' tool, which is designed to find available flights. Given the context, the user has requested information for the date '2025-05-29'. I will provide the date as a parameter to the 'flights' tool. After the tool returns the result, I will interpret the output and present the final answer to the user in a clear and concise manner.

Response: The final answer is the list of available flights from London to Paris for the date '2025-05-29'