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

1044

Output Tokens

396

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: Looking at the context, the user wants to find information about available flights from London to Paris for tomorrow. From the trajectory, we can see that: 1) The user requested flight information, 2) The system determined that tomorrow's date was needed first, 3) The calendar tool was called and returned '2025-05-29' as tomorrow's date. Now that we have the date (2025-05-29), the next logical step is to call the flights tool with the required parameters: from_city: London, to_city: Paris, and date: 2025-05-29. This directly addresses the user's original request for flight information.

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