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

2740

Output Tokens

350

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: We are in cycle 25 and the high‑level plan defined 5 steps. According to the scratch_pad we have already completed steps 1‑3 and are currently at step 4 (publish the review). After publishing we still need to update the scratch_pad and mark the task as complete (step 5). This means only one or two more actions (and thus cycles) remain before the plan is finished. Therefore we are still on track and have only a few cycles left.

Response: less than 10 only few left