self-improving-agent

Enforcing verification steps requires a structured execute, verify, retry, and extract learning loop before marking any task complete. This approach prevents premature completion by mandating validation checks and capturing actionable learnings immediately after task execution.

Logging decisions in agent workflows requires a structured format capturing ACTION, Data, Impact, and Effort fields. This records approvals or rejections to decision logs with timestamps and categories, preventing knowledge loss and accelerating future task execution.

Recording incident logs with timestamps involves appending categorized incident or decision entries to memory files. This creates a persistent audit trail by logging verification outcomes and structured learnings immediately after task completion.

Automated decision logging applies to multi-step tasks including deployments, cron creation, file updates, and API changes. It enforces a structured decision format and writes entries to memory files, ensuring verification happens before task completion.

Task verification fails when workflows lack enforced verification steps and structured learning capture. Without mandating execute, verify, and retry cycles before completion, actionable learnings and decision outcomes are lost, making future work slower and less reliable.