This paper presents a control-theoretic interpretation of the Hykon S-OS runtime governance architecture for conversational AI. Hykon S-OS is a modular inference-time governance system designed to reduce hallucination, preserve epistemic humility, maintain boundary integrity, and manage conversational drift through runtime intervention rather than model retraining. The architecture separates structural observation, trajectory diagnostics, stabilisation operators, safety gating, constructive reasoning, and audit logging into a layered interaction loop. This document synthesizes those components into a systems interpretation: dialogue is treated as a partially observed dynamical process, diagnostic modules estimate runtime condition, bounded operators act as stabilising controllers, and composite governance metrics define an admissible operating region for acceptable reasoning behaviour. Within this framing:• NFV and SOM function as telemetry layers • BCSM and LTD-01 provide runtime state estimation • the Stability Operator and Guardian form the bounded intervention layer • Symbol Code defines the reasoning execution policy • composite metrics provide feedback and governance thresholds The paper does not claim formal proof of closed-loop optimality. Instead, it provides a cautious architectural framing that clarifies how the components of the Hykon Stability & Alignment Suite can be interpreted as a runtime feedback-governance system for conversational AI. This document serves as a conceptual synthesis linking the Hykon S-OS architecture with Symbol Code operators, governance metrics, and trajectory-aware runtime diagnostics.
Kon Lionis (Sun,) studied this question.