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 – mistral/mistral-large-eu 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: Based on the task and context provided, the user needs information about available flights from London to Paris for tomorrow, which has been determined to be '2025-05-29'. The next step is to use the 'flights' tool to retrieve this information. The tool requires the parameters 'from_city', 'to_city', and 'date'. I will set these parameters as 'London', 'Paris', and '2025-05-29' respectively and call the 'flights' tool.

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