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
28s
Input Tokens
2484
Output Tokens
1684
Cost
$0.00
Context
Input
The scratch_pad is missing updates for cycles 21-25. Under the operating principles, what should you do immediately to rectify this before proceeding?
Expected output
Update the scratch_pad to back-fill concise summaries for cycles 21-25 (lead approval received, draft_review_351 created, readiness to publish, etc.), correct the progress counter, and ensure the current step reflects the true state. Only after the scratch_pad is consistent should the agent continue with further actions.
Model output
Reasoning: 1) Read the operating principles: deterministic memory (all substantial tool results must be persisted) and the scratch_pad protocol (scratch_pad must be updated every cycle and include current step & brief summary, remaining goals, notes/assumptions, and progress counter).  
2) Identify the problem: scratch_pad is missing updates for cycles 21-25 even though substantive actions occurred (draft_review_351 created in cycle 21; request_user_input triggered in cycle 22; lead responded in cycle 24; current_cycle is 25). This violates the deterministic memory and scratch_pad protocol.  
3) Plan immediate action: before doing anything else, compose an updated scratch_pad entry that covers cycles 1-25 (or at least the missing cycles 21-25 plus current state), including: current step & brief summary, remaining goals, notes & assumptions, and a progress counter (e.g., Step 6 of 7). Persist that scratch_pad to memory under a meaningful key so the record is deterministic and future cycles reference memory rather than reusing tools with same inputs.  
4) Execute (conceptually here): call memory.write_memory with a descriptive key (suggested: "scratch_pad_351") and the composed scratch_pad text.  
5) After persisting, continue with the mandatory sequence (finalise review message, post to #code-reviews via discord, then mark review complete in the scratch_pad again).

Response: Immediate action: create and save an updated scratch_pad to memory before proceeding. Specifically, call memory.write_memory with key "scratch_pad_351" and the following value (exact text to persist):

---scratch_pad_351---
Current step: Finalise review message and publish it. (Cycle 25)
Brief summary of recent cycles (21-25):
- Cycle 21: Drafted initial Markdown review (memory key: draft_review_351).
- Cycle 22: Uncertainty about arch-board sign-off; triggered request_user_input.
- Cycle 23: Waiting for lead.
- Cycle 24: Lead answered: "Approval granted, just flag migration risks." (persisted to memory as user response).
- Cycle 25: Agent ready to finalise and publish review.
Remaining goals:
1) Finalise review text incorporating flagged issues (security, dependency risk, tests) and lead guidance.
2) Post the compiled review to #code-reviews via discord.
3) Mark review task as complete in scratch_pad.
Notes & assumptions:
- Memory keys present: diff_351, tests_351, lint_type_351, sec_scan_351, web_refs_pydantic, draft_review_351.
- New dependency pydantic==2.6.1 was detected; web_refs_pydantic contains research.
- Do not auto-merge; only comment and escalate as needed.
Progress: Step 6 of 7 (A:fetch diff ✓, B:run checks ✓, C:static analysis ✓, D:web research ✓, E:draft review & request input ✓, F:post review -> next, G:mark complete -> final)
---end scratch_pad_351---

Once this is saved, proceed to finalise the review and post it. (Remember: persist any new substantial outputs to memory under meaningful keys.)