Intent-Aware Continuity in Evolving AI Systems: A Framework for Operational Provenance and Autonomous Governance

Modern AI systems evolve continuously through retraining, fine-tuning, policy revision, memory editing, tool replacement, and changes in operational context. These changes create an operational version of the Ship of Theseus problem: when should an updated system still count as the same governed entity, and when does it fracture into a categorically new one? This paper introduces the two-layer Intent-Aware Continuity Framework. The first layer models observable system behavior as a trajectory in a metric space over architecture, weights, policy, context or memory, and tools. This Observable Configuration State Space supports the precise measurement of Configuration Drift, stability monitoring, repair analysis, and controlled amendment of the system baseline. The second layer introduces a typed continuity judgment that evaluates persistence relative to an entity kind and decision context. It utilizes hard invariants, cryptographically authenticated lineage (e.g., C2PA manifests), branch structure, and governance constraints to adjudicate identity. We mathematically demonstrate that scalar configuration distance alone is insufficient as a universal identity criterion, whereas the combined framework yields context-sensitive continuity judgments of SAME, REVIEW, or NEW. The resulting architecture translates abstract identity disputes into an operational, provenance-aware governance framework for enterprise AI lineage tracking, liability transfer, and intent-aware autonomous agent verification.