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 Task | Opper AI

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Context

Input

Asses the current state and decide if we are on track or not

Expected output

We are on track

Model output

Reasoning: 1) User intent: Find available flights from London to Paris for tomorrow. 2) Current state: We already used the calendar tool and obtained tomorrow’s date as 2025-05-29. The date is in the required YYYY-MM-DD format for the flights tool. 3) Remaining goals: a) Use the flights tool with from_city=London, to_city=Paris, date=2025-05-29; b) Store the results; c) Present a clear summary to the user. 4) Checks: Tools are available; parameters are clear; no blockers. Potential refinement (if needed later): confirm timezone if “tomorrow” depends on the user’s locale, and optionally narrow airports or time windows. 5) Conclusion: We are on track, ready to execute the next step by calling the flights tool.

Response: On track. Next action: call the flights tool with {from_city: "London", to_city: "Paris", date: "2025-05-29"}. After retrieving results, store them in working memory and compile a concise summary for the user (including airlines, departure/arrival times, durations, and prices if available). Optional: confirm user’s preferred airports or time window if results are too broad.