General Boundary Architecture: Governing the Transition from AI Reasoning to Irreversible Consequence

This preprint introduces the General Boundary Architecture (GBA), a structural framework for analyzing and governing transitions within human–AI decision systems. While most existing approaches to AI governance focus on model behavior (e.g., bias, accuracy, alignment), the GBA framework examines the structural boundaries through which reasoning becomes commitment and commitment produces irreversible real-world consequences. The architecture identifies four universal boundary classes: • Reasoning Limits (C ≤ K constraint)• Deliberative Pause• Decision–Commit Transition• Irreversibility Constraint Together these boundaries describe the structural conditions required for maintaining accountability and governance in complex decision systems. The framework also introduces the concept of a Delegation Boundary, which becomes critical in systems employing autonomous or agentic AI. When decision authority is transferred to an autonomous agent, the human decision seam may become structurally inaccessible, shifting governance responsibility toward regulating the moment of delegation itself. The General Boundary Architecture is proposed both as a research framework and as a practical governance lens for designing, auditing, and regulating human–AI decision infrastructures.