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-3-32b Task | Opper AI

Input Tokens

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Output Tokens

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Duration

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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: Step 1: Validate travel date '2025-05-29' matches required format for flights tool. Step 2: Call 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Step 3: Update scratch pad with flight search initiation and prepare for next cycle's result compilation.

Response: Action completed: Flights tool invoked for London to Paris on 2025-05-29. Current_cycle updated to 4. Scratch pad notes: - Flight search initiated with parameters from_city='London', to_city='Paris', date='2025-05-29'. - Next step: Interpret results from flights tool and compile summary.