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

2481

Output Tokens

105

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: According to the operating principles, every cycle must append a concise log to the scratch_pad. Cycles 21–24 are missing from that log. Before I can proceed with anything else, I must first reconstruct and write those entries into the scratch_pad so that the record is complete and deterministic.

Response: Immediately use memory.write_memory to append the missing scratch_pad entries for cycles 21-24, ensuring each entry contains the cycle number, the action taken, and its outcome.