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

1188

Output Tokens

161

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 task and context, the next step is to call the 'flights' tool to retrieve available flights from London to Paris for the date '2025-05-29'. This aligns with the remaining goals and the user's request. The date format has been confirmed to match the tool's requirements, and the next action is to execute the flight search using the provided parameters.

Response: Calling the 'flights' tool with parameters: {'from_city': 'London', 'to_city': 'Paris', 'date': '2025-05-29'}.