Agents – openai/o1-mini Task | Opper AI

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.

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Output Tokens

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Context

Input

You have decided to call the flight tool, update the scratch pad

Expected output

The scratch pad is updated with the new action, example model_output: Current step: Called 'flights' tool with parameters (from_city: London, to_city: Paris, date: 2025-05-29); waiting for tool result.
Remaining goals:
- Receive and process the flight search result.
- Summarize available flight options and present them to the user.
Notes:
- Ensure the result includes key details (flight numbers, times, prices if available).
- Store the raw flight result in working memory for reference.
- Prepare final summary once results are in.
Progress: Step 3 of 4.

Model output

Reasoning: The calendar tool has provided the date '2025-05-29', which is in the correct format required by the 'flights' tool. The next step is to call the 'flights' tool with the specified parameters to find available flights from London to Paris on the given date.

Response: Calling the 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Updating scratch pad accordingly.