A Unified Policy Architecture (UPA): The Governance Kernel for Enterprise AI Operating Systems

Abstract:Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows, and collaborate with other agents. This shift creates a new governance challenge: existing authorization, security, guardrails, and compliance mechanisms are fragmented and are not designed to govern autonomous AI as a unified system. This paper introduces the Unified Policy Architecture (UPA), a governance architecture for Enterprise AI Operating Systems. UPA provides a unified policy model for governing AI and agents, tools, workflows, memory, enterprise resources, and agent-to-agent interactions and enterprise business rules. It extends policy control beyond authorisation to include runtime obligations, human approvals, compliance, audit evidence, and governance evaluation. We present UPA's governance model, declarative policy language foundations, policy evaluation semantics, extensible plugins, industry policy packs, and an evaluation framework for enterprise governance. We also identify extensions for multi-agent coordination, provenance-aware policies, and stateful runtime governance. UPA provides a foundation for building secure, accountable, and governable Enterprise Operating Systems for autonomous AI.

58 pages, 6 figures. Includes appendices with the DGPL grammar, SID registry, EAGBench benchmark specification, extended governance models, and policy examples

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)

arXiv:2609.06543 [cs.AI]

(or arXiv:2609.06543v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.06543 arXiv-issued DOI via DataCite (pending registration)