common-learning-log

Automatic learning log entries capture AI agent mistakes by recording structured details like the date, task, signal, mistake, pattern to avoid, and better approach into AGENTS_LEARNING.md for future review.

The structured format for logging AI agent errors includes the date, task, signal, mistake made, pattern to avoid, and better approach. This ensures consistent and detailed entries for each session retrospective or user correction.

Automatic learning log entries are triggered by predefined signals including pre-write violations, user corrections, and session retrospectives. These triggers initiate the logging process when specific keywords are detected.

Yes, the AGENTS_LEARNING.md file must be present. The automated logging process requires this file to append structured entries whenever an AI agent makes a mistake during a session.

The best way to record AI agent session retrospectives is to automatically append structured entries to a learning log. This captures the mistake, pattern to avoid, and better approach for ongoing self-improvement.

Yes, automated logging can track pre-write violations in AI agents. It detects these violations as predefined triggers and logs the mistake details into a structured learning log for future reference.