Maxwellian Governance for AI Systems: Unifying Semantic and Operational Fields into Gauge Layers for Permissioned Autonomy

AI systems are increasingly described as assemblies of components — models, tools, agents, and services — yet many of their failures are not component failures. They are field failures: the same capability behaves differently across environments, audiences, incentives, and time windows. In this paper we propose a Maxwellian governance lens in which AI is treated as a family of coupled fields (distributed quantities over situations) constrained by higher-order gauge layers that enforce permission, reversibility, and replayable justification. We generalize Large Language Models as one instance of signals within a broader class of Large Signals Models (LSMs), and place both language and non-language intelligence inside Large Signals Fields (LSFs), where admissibility is decided by typed boundaries, corridors, and receipts. The central contribution is a compact governance vocabulary that aligns semantic and operational design: fields describe how capability distributes and flows; gauge layers decide what movements are allowed; receipts make decisions replayable; and hesitation/rollback become first-class control rather than emergency patches. We outline how this lens reframes autonomy as a permissioned property that scales only when proofs remain portable under change.