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

12s

Input Tokens

1310

Output Tokens

1012

Cost

$0.00

Context

Input

Asses the current state and decide if we are on track or not

Expected output

We are on track

Model output

Reasoning: 1. **Analyze User Goal:** The user wants flight information from London to Paris for 'tomorrow'.
2. **Review Past Actions:** The agent recognized the need to convert 'tomorrow' into a specific date and successfully used the `calendar` tool to get '2025-05-29'.
3. **Evaluate Current State:** The agent now possesses all required parameters for the `flights` tool: `from_city` (London), `to_city` (Paris), and `date` (2025-05-29).
4. **Check Plan:** The scratch pad correctly lists the next step as using the `flights` tool. This is the most direct and effective path.
5. **Conclusion:** The logic is sound, the prerequisite data has been gathered without error, and the next action is clearly defined and actionable.

Response: The current state is **On Track**. The agent has successfully resolved the relative date dependency ('tomorrow' -> '2025-05-29') and has correctly updated its plan to utilize the `flights` tool with the specific parameters required (London, Paris, 2025-05-29). The workflow is proceeding logically toward the user's goal.