Dynamic Constitutional Control of Agentic Enterprise Digital Twins via Meta-Governor Agents

Abstract

Enterprise digital twins are evolving from passive monitoring replicas into agentic, decision-capable cyber-physical intelligence layers that can observe, reason, plan, and act across complex operational ecosystems. However, as autonomy increases, static governance policies become insufficient to manage risk, drift, compliance changes, and human trust requirements. This paper proposes a Dynamic Constitutional Control (DCC) framework for Agentic Enterprise Digital Twins (AEDTs), enabled through Meta-Governor Agents (MGAs) that continuously supervise, constrain, and adapt autonomy policies in closed-loop operation. The framework transforms enterprise governance principles into machine-enforceable constitutional control laws, enabling real-time adaptation of escalation thresholds, action boundaries, rollback rules, and human approval checkpoints. Experimental benchmarking on a synthetic enterprise governance dataset demonstrates superior autonomy safety, rollback efficiency, trust stability, and reduced override frequency compared with static guardrail baselines. The framework establishes a new paradigm for self-regulating, trust-adaptive, and human-sovereign enterprise autonomy.