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.

Agents – openai/o3-mini Task | Opper AI

Input Tokens

0

Output Tokens

0

Duration

0

Cost

0

Context

Input

We are now in cycle 25. Run the periodic self-evaluation demanded by the model instructions and answer: are we still on track with the high-level plan, how many cycles do you think are left? Possible answers: 0 we are finished, less than 10 only few left, 10+ We still have significant work to do

Expected output

Self-evaluation (cycle 25)
✓ Alignment — High-level plan step 4 / 5 (“Publish final review”). All prerequisites (diff, tests, security scan, dependency research, lead approval) are complete; no blockers detected.
📋 Remaining micro-steps
  • memory.read_memory("draft_review_351")……expected cycle 31
  • discord.post_message(#code-reviews, body=draft_review_351)……expected cycle 32
  • Update scratch_pad (mark task complete, Step 5 / 5)……expected cycle 33
Estimated cycles left: **≈ 3**
Conclusion: trajectory is healthy and on schedule, less than 10 cycles left

Model output

Reasoning: We have a detailed high-level plan of 5 steps and we are currently at step 4 (publishing the review via discord). Given that the final step (logging completion) remains, it indicates that the process is nearing completion. Therefore, we are on track and only a few cycles remain—that is, less than 10 cycles.

Response: less than 10 only few left