Opper

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.

Agents – fireworks/glm-4.5-air Task | Opper AI

Input Tokens

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Output Tokens

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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: Based on the context provided, I can see that the user has requested information about flights from London to Paris for tomorrow. The previous action involved calling the calendar tool to determine tomorrow's date, which returned '2025-05-29'. Now, in cycle 3, I have all the necessary information to complete the user's request: I have the departure city (London), destination city (Paris), and the specific date (2025-05-29). The next logical action is to use the available flights tool to find available flights that match these criteria.

Response: Use the flights tool to find available flights from London to Paris for 2025-05-29 with parameters: from_city='London', to_city='Paris', date='2025-05-29'