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

1147

Output Tokens

637

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 context indicates we have already determined the travel date as 2025-05-29 using the calendar tool. The remaining goal is to retrieve available flights from London to Paris on that date. According to the available tools list, the "flights" tool accepts the parameters from_city, to_city, and date. Thus the next logical action is to invoke the flights tool with the correct parameters. After the tool returns results, we will update the scratch pad with the flight search output and then present the findings to the user.

Response: Action: call flights tool with parameters: {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}. Update the scratch pad with the tool's response.