HAERS: Hierarchical Agent Error Regulation System - A Supervisory Control Framework for Human Feedback Systems, AI Alignment, and Organizational Stability

HAERS (Hierarchical Agent Error Regulation System) introduces a supervisory control framework addressing a structural gap in AI alignment and organizational leadership: how systems behave after divergence occurs under interdependence. Current alignment approaches optimize for what systems should output. They do not account for post-reflex control selection - the mechanism that determines whether a system defaults to correction-first or verification-first strategies after stress is activated. This distinction governs whether divergence escalates or stabilizes, and operates regardless of intent, intelligence, or values. The framework integrates predictive processing, HPA-axis stress dynamics, polyvagal theory, supervisory control theory, and conversational grounding into a single mechanistic architecture. It formalizes two biobehavioral cascades - the Stress Response Cascade (SRC) and Reward Response Cascade (RRC) - and identifies verification-first control selection as the critical missing layer in both AI deployment and human leadership systems. HAERS generates testable predictions across behavioral, physiological, and computational domains, has been validated through real-world prototype deployment, and is fully complementary to existing alignment approaches including Constitutional AI and RLHF. Framework details available for research discussion or organizational engagement under NDA. Contact: jbazbaz22@gmail.com or joseph@emotional-logic.consulting