从权限中分离能力：智能AI自主水平的治理框架
Separating Capability from Permission: A Governance Framework for Agentic AI Autonomy Levels
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中文总结 AI 辅助
本文针对人工智能系统自主性讨论中能力与权限混淆的问题，提出治理框架，区分允许的自主水平与自主能力水平，给出结构化自主水平，还提出风险感知决策过程，通过实例展示应用并提供实用指导。
中文摘要 AI 辅助
随着人工智能系统越来越多地表现出智能行为，关于自主性的讨论常常将系统在技术上能够做的事情与它们在实际中应该被允许做的事情混为一谈。本文引入了一个治理框架，该框架明确地将允许的自主水平（AAL）与自主能力水平（ACL）区分开来。AAL定义了在考虑风险、监督和问责的情况下，人工智能代理被授权行使的自主程度，而ACL则表征了代理的固有技术能力。我们提出了一套结构化的自主水平，涵盖反应式执行、决策支持、监督行动、目标导向自主和委托运营权限，并描述了随着自主性的增加，控制、可逆性和问责制是如何变化的。为了实施这个框架，我们提出了一个用于分配允许自主性的风险感知决策过程，分析了风险和问责制在自主水平上的演变，并通过一个已部署的企业数据工程代理展示了它的应用，说明了如何根据风险、可逆性和组织准备情况，将一个在高能力水平评估的系统有意地限制在较低的允许自主水平。通过区分授权和能力，这项工作为智能AI系统的设计、部署和治理提供了实用指导。
英文摘要
As AI systems increasingly exhibit agentic behavior, discussions of autonomy often conflate what systems are technically capable of doing with what they should be permitted to do in practice. This paper introduces a governance framework that explicitly separates Allowed Autonomy Levels (AAL), which define the degree of autonomy an AI agent is authorized to exercise given risk, oversight, and accountability considerations, from Autonomous Capability Levels (ACL), which characterize an agent's inherent technical abilities. We present a structured set of autonomy levels spanning reactive execution, decision support, supervised action, goal-directed autonomy, and delegated operational authority, and describe how control, reversibility, and accountability change as autonomy increases. To operationalize this framework, we propose a risk-aware decision process for assigning allowed autonomy, analyze how risk and accountability evolve across autonomy levels, and demonstrate its application through a deployed enterprise data engineering agent, illustrating how a system assessed at a high capability level can be deliberately constrained to a lower allowed autonomy based on risk, reversibility, and organizational readiness. By distinguishing authorization from capability, this work provides practical guidance for the design, deployment, and governance of Agentic AI systems.
