Change CADE only when evidence justifies changing the system
Context
A reusable training framework needs stability. If every execution creates ad hoc changes, the core training effect erodes and future adopters cannot tell which parts are canonical.
Decision
Treat structural change as controlled evolution: update CADE only when live evidence, better training research, or stakeholder requirements justify a specific change.
Alternatives Considered
Continuously revise the framework after every preference or comment
Pros
- Highly responsive
- Captures many ideas quickly
Cons
- Creates churn
- Weakens the baseline
- Makes the training effect harder to preserve
Freeze the framework completely
Pros
- Maximum stability
- Easy to teach and repeat
Cons
- Ignores execution evidence
- Prevents improvement when real failure modes appear
Reasoning
CADE needs deliberate stability, not stagnation. Evidence-driven change preserves the framework while allowing it to mature from what actually happens in execution.
AI Operator Skill Demonstrated
Using evidence to govern iteration instead of optimizing for polish
This decision keeps CADE from becoming a pile of one-off improvements. The point is to improve the system without losing the system.