The AI Bounded Corridor: An Applied Declared-Regime Model for Identity Kernels in Consequential AI Systems

Consequential AI systems increasingly depend upon claims that decisions, tasks, authorities, policies, memories, and artifacts remain the same across transformation. This paper develops an applied architecture for those claims by instantiating the Identity–Persistence Program’s forcing theorems within AI systems rather than extending the underlying formal theory. The proposed architecture separates probabilistic discovery from deterministic adjudication. Probabilistic components estimate candidate regimes and generate candidate continuations, while deterministic identity kernels evaluate persistence under declared regimes before proof gates license consequential action. The resulting inversion shifts optimization from search toward bounded adjudication under explicit structural assumptions. Identity kernels, governed memory, retraction propagation, runtime admission, federation, and persistent governance are presented as coordinated architectural components operating over declared regimes. Seven regime templates demonstrate how the same forced structural roles instantiate across distinct classes of consequential AI persistence claims while preserving strict separation between formal results, engineering embodiments, and empirical questions. The paper argues that identity enforcement constitutes an architectural category distinct from observability, evaluation, provenance, or governance tooling. Throughout, claims are explicitly bounded: PASS denotes licensed admissibility under a declared regime rather than truth; hallucinations are not prevented but prevented from becoming consequential when they fail declared admission criteria; and the architecture remains silent on ontological questions outside its declared scope. The result is an applied framework for governing consequential AI through deterministic identity enforcement over probabilistic generation.