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.

Duration

22s

Input Tokens

1709

Output Tokens

265

Cost

$0.00

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 Recap**: 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 direct solution, as it is designed to fetch flight data given departure/arrival cities and a date. No other tools are needed at this stage.
    3. **Parameters Ready**: All required parameters for the `flights` tool are available:
       - `from_city`: London
       - `to_city`: Paris
       - `date`: 2025-05-29
    4. **Next Step**: Call the `flights` tool with these parameters to retrieve available flights. This aligns with the goal of efficiency and avoiding unnecessary loops.
    

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