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

Input Tokens

1191

Output Tokens

275

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: The user's task is to find available flights from London to Paris for 'tomorrow'.
1. **Analyze Context & Trajectory:** The previous cycle determined 'tomorrow's' date using the `calendar` tool, which returned '2025-05-29'.
2. **Determine Next Step:** Now that the origin city ('London'), destination city ('Paris'), and date ('2025-05-29') are known, the next logical step is to use the `flights` tool to search for the required flights.
3. **Tool Parameters:** The `flights` tool requires `from_city`, `to_city`, and `date`.
    *   `from_city`: 'London'
    *   `to_city`: 'Paris'
    *   `date`: '2025-05-29'
4. **Action Formulation:** The next action should be a call to the `flights` tool with these parameters.

Response: call flights tool with {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}