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

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: 1. The task is to perform a periodic self-evaluation to determine if we are still on track with the high-level plan and estimate how many cycles are left.
2. From the context, we are currently in cycle 25, and the high-level plan consists of 5 steps.
3. Progress so far: Steps 1, 2, and 3 are completed, and we are currently working on Step 4, which involves publishing the final review.
4. After Step 4, only Step 5 (logging completion) remains, which should take minimal cycles.
5. Considering the progress and the remaining task, we estimate that fewer than 10 cycles are left to complete the plan.

Response: less than 10 only few left