Safe Action Policy Enforcement in Autonomous Decision-Making AI

Abstract

Ensuring safe decision-making in autonomous AI systems requires mechanisms that prevent agents
from selecting actions outside permitted operational boundaries, particularly in dynamic or uncertain
environments. This article investigates a structured enforcement framework where safety constraints
are embedded directly into policy computation rather than applied as post-hoc filters. The
methodology integrates constrained policy optimization, uncertainty-aware action gating, continuous
runtime state monitoring, and layered invariants within multi-stage workflows. Evaluation results
show that architectures with embedded safety logic maintain stable decision behavior and avoid
compounding unsafe effects, while reactive enforcement approaches exhibit delayed corrective
response and instability. The study concludes that safe action policy enforcement must be treated as an
integral design principle supported by persistent monitoring, traceability, and adaptive control
mechanisms.