Action-Bound AI Safety: A Pre-Commitment Runtime Framework for Physical, Cyber-Physical, and Transactional Systems

This manuscript proposes Action-Bound AI Safety, a pre-commitment runtime framework for physical, cyber-physical, transactional, and agentic systems. The central claim is narrow: runtime oversight can improve intervention success when monitoring occurs before externally consequential commitment, provided three conditions remain available — usable signal, enough time, and retained intervention authority. The framework introduces commitment boundaries, pre-action buffers, phase-sensitive escalation, Safety Slack (S_t), and commitment gates. It treats runtime safety as a control problem: whether the system can detect risk early enough, interpret it reliably enough, and still possess authority to halt, gate, roll…