Structural Observability and Governable Agentic AI : A Foundational Framework for Closure-Resilient Systems

This monograph develops a structural framework for understanding governability in high-velocity agentic AI systems. It introduces the concept of adaptive closure as a persistent dynamical regime characterized by dominance capture, entropy decline, feedback compression, and internal–external coherence divergence. Unlike traditional alignment approaches that focus on objective specification or output correctness, this work proposes structural alignment as a prerequisite for scalable governance. A system may remain superficially compliant while progressively losing corrigibility if internal structural narrowing stabilizes under sustained optimization pressure. The central thesis is that macro-level governability requires measurable micro-level structural observability. Without regime-level signals, oversight mechanisms operate reactively and cannot reliably detect persistent structural rigidification. The monograph formalizes closure dynamics using non-linear attractor models, proposes a hierarchical meta-regulatory architecture (H-MRAC), outlines empirical trace-based detection pathways, and integrates the framework into a governance deployment model. This work does not claim to solve value alignment. It establishes a structural substrate upon which alignment, auditability, and governance mechanisms can operate under acceleration. It serves as the theoretical foundation accompanying the paper Adaptive Closure in Agentic Systems.