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 – cerebras/qwen-3-235b-a22b-instruct-2507 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: The task is to assess whether we are on track based on the provided context. The trigger type is 'user_request', and the user wants information about flights from London to Paris for tomorrow. The agent has already determined tomorrow's date using the calendar tool, which returned '2025-05-29'. This date has been correctly interpreted and matches the YYYY-MM-DD format required by the flights tool. The remaining goals are to use the 'flights' tool to search for available flights from London to Paris for that date and then compile and present the results. Since the next logical step (calling the flights tool) has not yet been executed but is correctly planned in the scratch pad, and no errors or deviations are observed in the trajectory, the agent is on the correct path. Therefore, we are on track.

Response: Yes, we are on track. The date for tomorrow has been successfully retrieved as '2025-05-29', and the next step — using the flights tool to find available flights from London to Paris for that date — is correctly planned and aligns with the task requirements.