This framework introduces the concepts of Nomological Determination and Ontological Techno-regulation for safety-critical AI systems. Shifting away from stochastic models prone to 'hallucinations,' QBI-CORE AIC leverages the Kuramoto Model to achieve a physical decision-making consensus through Phase Synchronization. The architecture defines the Order Parameter R as a deterministic metric for compliance with the EU AI Act, ensuring that the system's physical state inherently aligns with regulatory validation. Furthermore, the paper details the optimization of Phase Lag (\alpha) to enable fluid obstacle avoidance without compromising the mathematical traceability of the safety kernel. This approach transforms Cognitive Governance from a bureaucratic layer into an integrated cognitive infrastructure.