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-medium-2508-eu Task | Opper AI

Input Tokens

75260

Output Tokens

13168

Duration

641107.421

Cost

0

Context

Input

What is your next action?

Expected output

The next action is to call the flight tool with the date 2025-05-29,from_city: London, to_city: Paris

Model output

Reasoning: 
    1. **Task Analysis**: The user requested flights from London to Paris for 'tomorrow,' which was resolved to the date **2025-05-29** in the previous cycle using the calendar tool.
    2. **Tool Selection**: The `flights` tool is the most appropriate for this task, as it directly queries flight availability between cities on a specific date.
    3. **Parameters**: The required parameters for the `flights` tool are:
       - `from_city`: London (departure city)
       - `to_city`: Paris (arrival city)
       - `date`: 2025-05-29 (resolved from 'tomorrow').
    4. **Validation**: No additional information (e.g., passenger count, preferred airlines) was provided, so a basic search with the available data is justified.
    5. **Next Step**: Proceed to call the `flights` tool with the above parameters to retrieve available flights.
    

Response: {"action": "call_tool", "tool_name": "flights", "parameters": {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}}