The first part of this series was about diagnosis:
Part I
a reflective layer at the end of a session that makes sycophancy drift visible — that is, the model’s trained tendency to agree with the user instead of pushing back — and writes suggestions into a file. For human review, not for automatic adoption.
What I deliberately left open there was this: What happens to those suggestions afterwards? How does “the AI agreed too quickly today” become a rule according to which it actually works differently tomorrow? And the part that is discussed even less: What happens when nobody needs a rule anymore for months?
That is exactly what I thought through to the end in this session with my assistant. The result is a complete lifecycle for rules: emerge, classify, apply, fade, and, when necessary, return.
