发表机构
University of Miami; Columbia University; Stanford University（迈阿密大学; 哥伦比亚大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AI-GRACE框架，将组织目标与义务转化为智能体AI用例的部署能力与架构，通过风险域、保证需求及运行包络实现可追溯治理。
AI 中文摘要
部署智能体人工智能的组织必须确定的不仅仅是模型是否可信；它们还必须确定对于某个用例，需要验证、控制和观察什么，以便在履行适用义务的同时实现预期结果。本文提出AI-GRACE（智能体智能治理、风险、保证、控制与证据）作为一个用例操作化框架，将组织治理与技术实施连接起来。该提案基于专业观察和对标准及文献的有目的综合，使用设计科学来构建方法贡献的框架，并使用情境化方法工程来指导情境化定制和重用。该框架首先确立目标与义务，然后在七个提议的领域中评估风险，包括使命与价值实现。它推导出部署前的保证、运行时控制和证据的需求，这些需求指导能力资格认定、差距评估和逻辑架构。智能体运行包络规定了允许的操作和升级条件，而风险对齐的独立级别（RAIL）则总结了授权的独立性。一个虚构的零售银行应用示例说明了该方法。其贡献是为决定组织必须实施什么、已经支持什么以及仍有哪些未解决问题提供可追溯的基础。实证评估必须确定它是否能改善部署决策、效率和重用。
英文摘要
Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate, control, and observe for a use case to deliver its intended outcome while meeting applicable obligations. This paper proposes AI-GRACE (Agentic Intelligence-Governance, Risk, Assurance, Controls, and Evidence) as a use-case operationalization framework connecting organizational governance with technical implementation. The proposal draws on professional observations and a purposive synthesis of standards and literature, using design science to frame the method contribution and situational method engineering to guide contextual tailoring and reuse. The framework establishes objectives and obligations and then assesses risks in seven proposed domains, including mission and value realization. It derives requirements for assurance before deployment, runtime controls, and evidence, which guide capability qualification, gap assessment, and a logical architecture. An Agent Operating Envelope specifies permitted actions and escalation conditions, while Risk-Aligned Independence Levels (RAIL) summarize the authorized independence. A fictional retail banking application illustrates the method. The contribution is a traceable basis for deciding what an organization must implement, what it already supports, and what remains unresolved. Empirical evaluation must establish whether it improves deployment decisions, efficiency, and reuse.
Comments31 pages, 2 figures, 6 tables
