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 – openai/gpt-5-mini Task | Opper AI

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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: 1) Understand intent: The user asked to find available flights from London to Paris for tomorrow. 2) Check context: Previous cycle returned the date '2025-05-29' for tomorrow. 3) Check tools: A 'flights' tool is available and requires from_city, to_city, and date in YYYY-MM-DD format. 4) All required inputs are available (from_city = 'London', to_city = 'Paris', date = '2025-05-29'). 5) Plan: Directly invoke the flights tool with those parameters to obtain flight options. If the user has additional preferences (time window, airlines, nonstop), we can filter after retrieving results or ask a clarifying question, but the most direct next action is to call the flights tool now.

Response: Next action: call the flights tool with parameters {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}.