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.

Input Tokens

Output Tokens

Duration

Cost

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 the Goal:** The user wants to find flights from London to Paris for the next day.
2.  **Evaluate Past Actions:** The agent correctly identified that the first step was to determine the exact date for "tomorrow". It used the `calendar` tool, which successfully returned '2025-05-29'. This was the correct first step.
3.  **Assess the Current State:** The `scratch_pad` shows the agent is aware of the result from the calendar tool. The `Remaining goals` section clearly and correctly outlines the next logical step: use the `flights` tool with the parameters `from_city='London'`, `to_city='Paris'`, and the newly acquired `date='2025-05-29'`. 
4.  **Check for Errors/Deviation:** The plan is sound and directly progresses toward the user's goal. The agent has all the necessary information to call the next tool. There are no deviations or loops.
5.  **Conclusion:** The agent has successfully completed a prerequisite step and has a clear, correct plan for the next action. Therefore, the process is on track.

Response: The agent is on track. It has successfully completed the initial step of determining tomorrow's date ('2025-05-29') and has a clear and correct plan to use the `flights` tool with the appropriate parameters to fulfill the user's request.