Effect-Transparent Governance for AI Workflow Architectures: Semantic Preservation, Expressive Minimality, and Decidability Boundaries

This research presents a machine-checked formalization of AI workflow architectures with effect-transparent governance, demonstrating that governance can be imposed without losing computational expressivity. It defines a governance operator G for mediating effectful directives like memory access and LLM queries, proving seven key properties including governed Turing completeness and a decidability boundary.