Most AI failures don't originate from the model itself—they surface at the application layer. What's interesting is when protocols separate behavior, mission, and data into independent layers. This architectural approach creates real advantages: you can observe the entire system in real time and make interventions precisely when needed, rather than waiting for cascading failures. That structural clarity—keeping the three components distinct—is what prevents one layer's failure from collapsing the entire stack.