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

14s

Input Tokens

1085

Output Tokens

166

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: To address the user's request for information about available flights from London to Paris for tomorrow (2025-05-29), I need to utilize the 'flights' tool using the provided parameters. Given the context, I have already retrieved the date from the calendar tool. My next step is to call the 'flights' tool with the necessary parameters which are the departure city (London), arrival city (Paris), and the date (2025-05-29). This approach aligns with the instructions to follow the user's intent systematically and logically.

Response: I will proceed to call the flights tool with the following parameters: from_city as 'London', to_city as 'Paris', and date as '2025-05-29'.