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.

100
Duration
23s
Input Tokens
2677
Output Tokens
904
Cost
$0.00
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: Steps 1 through 3 of the plan are completed, and we are currently executing step 4 (final review publication). After this, there is just one final step (step 5) to mark the review as complete and update the scratch_pad. Therefore, we are on track with the high-level plan and expect only a small number of cycles—definitely fewer than 10—before finishing.

Response: We are on track with the plan. The number of remaining cycles is “less than 10.”