Adaptive Closure in Agentic Systems : Structural Metrics and a Meta-Regulatory Control Layer

This paper introduces adaptive closure, a structural dynamical regime that may emerge in agentic AI systems operating under sustained optimization pressure. Unlike value misalignment or reward misspecification, adaptive closure describes a persistent structural narrowing characterized by dominance capture, entropy decline, and feedback compression. The core paradigm shift proposed in this work is from output-level alignment to structural alignment. Traditional oversight mechanisms primarily evaluate outputs, behaviors, or reward signals. However, by the time misalignment becomes visible at the output level, structural rigidification may already be established internally. A system can remain superficially…