AGL-1：作为可信企业智能控制平面的企业人工智能治理层
AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligence
AI总结：
研究企业人工智能从实验走向运营面临的治理挑战，介绍AGL-1企业人工智能治理层这一参考模型，基于相关原则将治理问题从检索控制扩展到全执行路径治理，定义七个治理领域。
摘要：
企业人工智能正从孤立实验转向跨多种系统和工作流程的运营依赖。随着这一转变加速，主要挑战不再只是模型访问或推理规模，而是治理智能操作。本文介绍AGL-1企业人工智能治理层，它是跨多种组件的控制平面参考模型，基于相关原则扩展治理问题，识别失败模式并定义七个治理领域。核心观点是人工智能的持久企业价值 increasingly 取决于大规模治理智能的能力。在复杂企业中，信任不仅是模型的属性，更是模型周围系统的属性。
英文摘要：
Enterprise artificial intelligence is moving from isolated experimentation toward operational dependency across copilots, retrieval-augmented generation systems, autonomous agents, and AI-enabled business workflows. As this transition accelerates, the primary enterprise challenge is no longer only model access or inference scale. It is governed intelligence operations: the ability to enforce authorization, preserve contextual lineage, control persistent memory, detect stale or conflicting knowledge, constrain agentic execution, and produce audit-ready evidence across distributed AI estates. This paper introduces AGL-1, the Enterprise AI Governance Layer, as a vendor-neutral reference model for the control plane that should operate across foundation models, retrieval systems, orchestration frameworks, enterprise memory, policy engines, observability systems, tools, APIs, and business applications. Building on governed knowledge-system principles introduced in GKS-5, AGL-1 generalizes the governance problem from retrieval-specific controls to full AI execution-path governance. It identifies recurring failure modes such as unauthorized retrieval, stale grounding, unmanaged memory, weak provenance, policy drift, fragmented observability, and uncontrolled autonomous execution. It then defines seven governance domains: identity-aware retrieval, policy enforcement, provenance management, memory governance, knowledge integrity monitoring, agentic execution control, and trust observability. The central claim is that durable enterprise value from AI will increasingly depend on the ability to govern intelligence at scale. In complex enterprises, trust is not a property of the model alone. It is a property of the system around the model: identity, knowledge, policy, memory, tools, human oversight, and evidence working together as a managed control plane.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
