Opper

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.

Input Tokens

Output Tokens

Duration

Cost

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: Per the scratch_pad protocol (Mandatory Review Sequence G) and Operating Principle #2 (Deterministic Memory), every cycle must update the scratch_pad. Missing updates indicate incomplete compliance. To rectify:
1. Verify current scratch_pad state via memory.read_memory('scratch_pad')
2. Cross-reference with trajectory summary to identify gaps (cycles 21-25)
3. Reconstruct missing entries using trajectory data
4. Persist updated scratch_pad via memory.write_memory with detailed cycle entries
5. Confirm successful write before proceeding with Cycle 25 tasks

Response: Immediately use the memory tool to write missing scratch_pad entries for cycles 21-24 and the pending 25th cycle. Compose entries that follow the protocol: for each cycle, document steps taken, outcomes, and remaining goals. Example structure:

---
**Cycle 21**
- Step: Draft initial PR review
- Result: Markdown stored in draft_review_351
- Remaining: Arch-board signoff clarification
---
**Cycle 22**  
- Step: Requested human input (security/framework policy)
- Result: Triggered request_user_input with 'Does adding pydantic v2 require arch-board approval?'
- Waiting: Lead response
...

Persist via: github.memory.write_memory(key='scratch_pad', value=updated_content)