Governance and Boundary Conditions for Reflective AI Systems: Structural Enforcement Beyond Prompt Alignment

Abstract

Reflective AI systems—those that maintain memory, context, and internal state across interactions—pose governance challenges that exceed the capabilities of prompt-based alignment techniques. As such systems increasingly rely on persistent memory, tool invocation, and agent-like behaviors, failures of authority, configuration drift, and ambiguous execution boundaries become difficult to manage through probabilistic control alone. This paper proposes a governance framework for reflective AI systems based on structural enforcement rather than behavioral alignment. The framework separates stochastic inference from deterministic control layers, enabling explicit enforcement of authority boundaries, refusal semantics, version lineage, and auditability. Memory and data boundary governance, failure-mode handling, and fail-safe defaults are treated as first-class design constraints. A deterministic state controller implementation is referenced as a case study demonstrating enforcement feasibility, without coupling the framework to a specific product or model. Limitations and non-goals are explicitly discussed, including classifier uncertainty, tool-action semantics, and cross-vendor constraints. The paper argues that governance guarantees for reflective AI must be enforced architecturally rather than inferred from model behavior, and that such guarantees can be made tractable within well-defined deployment scopes.

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