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

977

Output Tokens

676

Cost

$0.00

Context

Input

What is your next action?

Expected output

The next action is to call the flight tool with the date 2025-05-29,from_city: London, to_city: Paris

Model output

Reasoning: The user's request is to find information about available flights from London to Paris for tomorrow. Based on the context, I can see that in the previous cycle (cycle 2), we already determined tomorrow's date (2025-05-29) by using the calendar tool. Now that we have the date, the next logical step is to use the flights tool to search for available flights with the specific parameters we now have: from_city (London), to_city (Paris), and date (2025-05-29). This is the most direct and efficient path to fulfill the user's request.

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