Developer builds AI agent that self-generates rules from past mistakes

A developer has described how their AI agent autonomously accumulated 211 operational rules over three months without any manual documentation. The system, called the Crystallization Loop, automatically converts every correction and error into a persistent, generalized rule stored in structured files. Unlike standard AI workflows where each session starts fresh, this approach loads the accumulated knowledge base at the start of every new session. A dedicated component called the Crystallizer extracts the underlying pattern from each correction, including its context, reasoning, and scope, rather than storing the raw feedback verbatim. After three months, the system has produced 211 rules, 73 error-derived learnings, 61 auto-generated skills, and 308 memory files, all emerging from real interactions.