The AI RBAC approach is the interesting part. By giving each model a different role with explicit constraints (Scout for research, Architect for specs, Coder for implementation), you're preventing the kind of context bleed that happens when one model tries to do everything. The strict "no implementation code" constraint on Claude is a good example of using prompt constraints as a boundary, not just a suggestion.

That constraint design is basically the same problem flompt solves on the prompt side. When you put role, objective, and constraints in a flat string, the model treats them as suggestions with varying weights. Named blocks with hard separations make them stick.

I built flompt (github.com/Nyrok/flompt) to do exactly that: decompose prompts into 12 typed semantic blocks (role, objective, constraints, output format, etc.) and compile to Claude-optimized XML. Open-source.