211 kristallisierte Regeln
Wie mein Agent aus 211 Fehlern ein besseres System geworden ist als ich es je programmieren könnte Heute Morgen hat mein Agent etwas getan, das er vor drei Monaten nicht konnte. Er hat einen eingehenden Webhook-Payload selbstständig klassifiziert, die richtige Skill-Route gewählt und dabei einen Edge Case abgefangen, den ich nie explizit beschrieben hatte. Ich habe das erst bemerkt, als ich die…
A system developed by an agent has become better than what its creator could program in three months. The agent classified a webhook payload on its own, selected the correct skill route, and caught an edge case that the creator never explicitly described. The agent applied a rule it never wrote, stemming from a mistake made on March 14th.
The Crystallization Loop has fundamentally changed how the creator thinks about AI systems. The Loop transforms every correction, feedback, and error into a persistent rule stored in a structured knowledge base. After three months, there are 211 crystallized rules, 73 learnings from real errors and corrections, 61 skills that emerged automatically from recurring tasks, and 308 memory files that permanently store the context of the project.
No document was written manually; all were generated from real interactions. The technical implementation consists of three components: the Feedback Collector, the Crystallizer, and the Memory Loader. The Feedback Collector triggers the collection process every time the agent is corrected or makes a mistake. The Crystallizer analyzes the correction and derives a generalizable rule from the pattern.
The Memory Loader loads relevant rules into each session based on the task type. The creator learned that the loop prevents mistakes and makes the agent more creative within its preferences, leading to faster and more decisive outputs.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
