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.

Duration

Input Tokens

2674

Output Tokens

143

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: Step-by-step evaluation against the high-level plan:
1. Gather diff & CI outputs – already completed (cycles 2–3).
2. Security + style analysis – completed (cycles 4–8).
3. Research new dependency – completed (cycles 9–20).
4. Publish final review – the agent is ready to post the review stored in memory key `draft_review_351`; this is the only remaining action before step 5.
5. Log completion – still pending but trivial.

Therefore, essentially only one micro-cycle (the current one) is needed to finish the review task.

Response: 0 we are finished