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

10s

Input Tokens

1139

Output Tokens

213

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: The user wants to find flights from London to Paris for tomorrow. The context indicates that the calendar tool has been called and returned '2025-05-29'. The next logical step is to use the 'flights' tool to search for flights. Based on the provided context, the 'flights' tool requires 'from_city', 'to_city', and 'date'. The 'from_city' is 'London', the 'to_city' is 'Paris', and the 'date' is '2025-05-29' from the calendar tool's output. Therefore, the next action is to call the 'flights' tool with these parameters.

Response: Action: call flights tool with parameters: {'from_city': 'London', 'to_city': 'Paris', 'date': '2025-05-29'}