Self-Improving

Continuous learning from AI errors is implemented by logging operational failures and user corrections into a structured archive. The system categorizes these lessons by priority and domain to prevent recurring errors in future AI workflows.

Managing lessons learned in AI development requires a hierarchical storage system using HOT, WARM, and COLD tiers. This approach organizes operational failures by priority and recurrence, ensuring critical knowledge is retained for continuous improvement.

Lesson promotion for recurring AI failures uses defined rules based on priority and recurrence to elevate lessons. Frequently recurring lessons are promoted into core AI behavior rules or known defects to permanently correct parsing and logic errors.

You should log a lesson learned whenever the AI system is corrected by a user or an operation fails. Recording these instances immediately captures the failure context, allowing the system to categorize and analyze the event for future error reduction.

Hierarchical storage supports knowledge management of AI defects by organizing lessons into HOT, WARM, and COLD tiers. This structured archive categorizes defects by domain and priority, enabling efficient retrieval and promotion of critical knowledge.

The Self-Improving Skill updates core AI behavior rules through its knowledge promotion process. When operational failures recur frequently, the system promotes the corrected logic into core configuration files like AGENTS.md or IDENTITY.md for future reference.