Legislating the Future for Physical AI in Agile Manufacturing: A Risk-Tiered Governance Framework for Factory Outputs, Actions, and IP

Abstract

Agile manufacturing is progressively being based on Physical AI agents, embodied (embodied) or semi-embodied systems like industrial robot platforms, collaborative robots, mobile manipulators, vision-guided inspection stations, edge-AI controllers that sense, reason and act in complex IT/OT systems. These agents are not only producing digital artifacts such as work instructions, robotic or PLC code, and CAD/CAM toolpaths, but also executing physical processes, the breakdown of which may lead to safety incidents, defects in quality and intellectual leakage. This review explains why traditional, case-by-case legal frameworks, be it those based on the doctrine of copyright, product-liability, or enforcing workplace safety regulations, put a burden on the situations of probabilistic autonomy, periodic software updates, and global supply chains. The analysis reveals that scalable governance requires ex-ante provisions to ensure such action risk: intensive documentation, validation sites, systematic surveillance, traceability mechanisms and prompt responsive notice and action processes to challenged outputs and risky actions by suggesting to reconsider a broader concern of factory production and factory output as output-side infringement. The legislative-governance framework suggested preserves a three-level risk logic and in line with the manufacturing environment. Tier 1 mitigates high-risk risky behaviours, and, high-confidence spillage of guarded industrial artifacts (e.g. verbatim proprietary code or toolpaths). Tier 2 includes uncertain behaviors of medium risks and contestable similarity. Tier3 refers to the general strategies and non sensitive advice with low risk. The framework distributes tasks through the manufacturing AI chain of supply, such as model provider, integrator, plant deployer, and operator, in line with functional control, and relates technical capabilities (learning, perception, shared autonomy, multi-agent orchestration) to concrete governance controls (safety envelopes, change management, audit trails, rollback plans). We provide an engineering-grounded definition of Physical AI in factory perception–planning–control architectures, operationalize a three-tier governance model using measurable triggers and audit-ready evidence artifacts, and illustrate how the proposed controls integrate with safety-certified architectures via safety envelopes and versioned change management.