This manuscript formalizes the 'Stability Standard,' a governance-as-code (GaaC) architecture designed to transition high-risk AI systems from probabilistic 'black-box' opacity to deterministic, auditable consistency. Addressing the 'velocity gap' between algorithmic deployment and regulatory oversight, the Stability Standard introduces three mandatory pillars for consequential decision-making: (1) Runtime Enforcement of Normative Constraints derived from applicable anti-discrimination law, (2) Glass-Box Transparency via contemporaneous Auditable Reasoning Traces, and (3) Cryptographically Unforgeable Decision Records. By re-situating algorithmic accountability as a prerequisite for deployment rather than a retrospective remedy, the Stability Standard provides an infrastructure-level solution to the systemic alignment failures currently characterizing enterprise AI integration.