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

31135

Output Tokens

4299

Duration

94087.7

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: Let me assess the current state of the task progress:

1. **Task**: Find information about available flights from London to Paris for tomorrow

2. **Current Progress**: 
   - We're at cycle 3 of the task execution
   - Step 2 of 4 has been completed
   - The calendar tool has been called and returned tomorrow's date: 2025-05-29
   - The date format matches the flights tool requirements (YYYY-MM-DD)

3. **Remaining Goals**:
   - Use 'flights' tool to get available flights from London to Paris for 2025-05-29
   - Compile and present the results to the user

4. **Next Action**: Based on the scratch_pad note "Interpret tool result (calendar returned '2025-05-29') and decide next action", we need to call the flights tool with the appropriate parameters.

5. **Assessment**: We are on track. The task is progressing according to plan - we've identified the date, and now we need to search for flights on that date. The necessary information (date, origin, destination) is available to call the flights tool.

Response: We are on track. The task is progressing as planned. We have successfully determined tomorrow's date (2025-05-29) using the calendar tool, and we are now ready to move to the next step which is using the flights tool to search for available flights from London to Paris on that date. All required information is available and the next action is clear.