这篇论文给了一套为智能体AI系统定风险等级的方法，有十二个打分维度，还专门考虑了编程助手场景，做AI治理的可以看看。
TrustX Agent Risk Classification Framework (ARC) 提出一种可重复的结构化方法，适用于七类智能体AI系统。框架核心是一个十二维度评分规则，结合GPA+IAT分类模型和五级自主性框架，输出三级治理输出与映射控制建议。还包含专门的编程助手扩展，处理此类AI系统的细微差别。该框架面向AI治理从业者、风险官、开发者和监管者，并计划定期迭代。
TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems
The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity of general-purpose AI risk frameworks to classify and govern them. In this paper, we introduce the TrustX Agent Risk Classification Framework, a structured, repeatable instrument that can be applied to seven types of agentic AI systems and is grounded in foundational pre-existing AI governance frameworks. At the core of the framework is a twelve-dimension scoring rubric that robustly quantifies the risk. This rubric is combined with other components, such as the GPA + IAT classification model and the five-level autonomy framework derived from existing literature. These inputs produce a three-tier governance output with mapped control recommendations. A specialised Coding Assistant extension is also included to account for nuances specific to this type of agentic AI system. We then use an illustrative example to show our framework in practice. ARC is intended for AI governance practitioners, risk officers, developers, and regulators, and it will regularly undergo iteration as we continue to expand it and make it more robust. The community can access the interactive framework here: https://arc.responsible.ai/