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

1045

Output Tokens

771

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: Analyzing the current state: The user requested flights from London to Paris for tomorrow. In previous cycles, we already invoked the calendar tool and obtained tomorrow's date as '2025-05-29'. Now we have all the required information to fulfill the user's request: departure city (London), arrival city (Paris), and travel date (2025-05-29). The flights tool is available and requires exactly these three parameters (from_city, to_city, date). Since we have completed the prerequisite step of determining the date, the logical next action is to invoke the flights tool with the gathered parameters to retrieve the available flight options.

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