The Missing Control Layer Between AI Decisions and Execution (github.com)

The recent announcement of the Execution Control Layer (ECL) repository introduces a formal architectural specification aimed at bridging AI decision-making and real-world execution. The ECL establishes a structured governance framework that ensures AI systems remain governable, auditable, and reproducible as they operate in dynamic environments. This deterministic control layer is defined by explicit architectural boundaries, invariants, and failure characteristics, allowing for a clear understanding of how AI-driven actions can be regulated and held accountable. Significantly, the ECL acts as a governance-first construct that binds execution to policy decisions, marking a pivotal shift in how AI systems are architected for compliance and oversight. While the repository does not offer operational guidance or code implementations, it serves as a citation-grade reference for defining the required architectural semantics and determinism guarantees that align AI executions with established governance frameworks. Future adaptations or derivatives must reference this specification to ensure consistency, making it a foundational element for developing robust AI governance solutions.