Most AI failures today are not model failures,they are control failures.

AI systems increasingly operate inside environments where decisions are not explicitly bounded. Without a defined Decision Boundary, systems continue execution beyond intended scope, often guided only by probabilistic outputs rather than enforced limits. A Control Signal—whether human-in-the-loop intervention, policy trigger, or system constraint—is what interrupts or redirects that flow. But in many implementations, that signal is either absent or non-binding. This leaves the Constraint Layer weak or symbolic, rather than operational. When the Constraint Layer is not enforced at execution-time, governance exists only as documentation, not behavior. Reframing Sentence Governance is not what you write into policy,it is what the system is structurally unable to do. Real-World Implication In enterprise AI deployments, this gap shows up as over-permissioned agents, silent data access, or untraceable decision paths. Without enforced Decision Boundaries and binding Control Signals, organizat