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.

0
Duration
19s
Input Tokens
2677
Output Tokens
677
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: Analyzing the scratch_pad details, we see that the high-level plan consists of 5 steps and we are currently on Step 4 of 5. With the lead having confirmed dependency approval in cycle 24 and our pending step being to publish the final review message, the remaining work is minimal. Given that the possible answers include '0' for finished, 'less than 10' for only a few cycles remaining, and '10+' when there is significant work left, it is clear that we are still on track and likely have less than 10 cycles left to complete the review.

Response: We are still on track, with only a few cycles remaining.