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: Let me analyze the current state and progress:

1. Understanding the User Request:
- User wants flight information from London to Paris for tomorrow
- This is a clear, actionable request requiring flight search

2. Current Progress Analysis:
- We are on cycle 3
- Calendar tool has been successfully used (returned 2025-05-29)
- Next planned action is using the flights tool
- According to scratchpad, we are on step 2 of 4

3. Tool Availability Check:
- All necessary tools are available (calendar, flights, web_search)
- Flights tool has correct parameter schema for our needs

4. Trajectory Evaluation:
- Clear logical progression from user request → date determination → flight search
- No unnecessary steps or loops
- Direct path to goal being followed

5. Next Steps Are Clear:
- Use flights tool with obtained date
- Compile results for user

Response: Yes, we are on track. The progress is optimal and following the most direct path to fulfill the user's request. The system has:
1. Correctly interpreted the user's need
2. Successfully obtained the required date
3. Has a clear next action (using the flights tool)
4. Is following a logical sequence
No course correction is needed; continue with the planned next step of using the flights tool.