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
40s
Input Tokens
2484
Output Tokens
327
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: The operating principles specify that:
1. We must maintain an up-to-date scratch_pad (Scratch_pad protocol).
2. Each cycle’s result must be recorded before the next action (Think-Plan-Act-Check loop).

Cycles 21-25 are missing, so proceeding would violate these principles. Therefore, the very next step is to restore compliance by immediately updating the scratch_pad with concise summaries for cycles 21-25 (current step, result, remaining goals, notes, Step x of 7, etc.) and saving it to memory via memory.write_memory. Only after the scratch_pad is complete can we continue with the review workflow.

Response: Update the scratch_pad right now with summaries of cycles 21–25 and save it to memory before doing anything else.