通过代码分析评估AI代理自主性，无需运行即可测风险。
Measuring AI agent autonomy: Towards a scalable approach with code inspection
- 基于代码结构评分，不依赖运行时观察。
- 用可扩展的分类体系衡量影响与控制程度。
- 适合安全评估、模型审计等需要低风险检测的场景。
AI代理是能够自主实现复杂目标的系统。评估其自主水平对理解潜在收益与风险至关重要。现有评估多聚焦特定风险，依赖运行时观测——即在任务执行过程中观察代理行为。本文提出一种基于代码的自主性评估方法，无需运行AI代理即可完成评估，从而降低运行成本与风险。通过该框架，可对运行AI代理的编排代码进行评分，依据包含影响与监督两个维度的分类体系量化自主属性。我们以AutoGen框架及其典型应用为例验证了该方法的有效性。
原文摘要 · Abstract (English)
AI agents are AI systems that can achieve complex goals autonomously. Assessing the level of agent autonomy is crucial for understanding both their potential benefits and risks. Current assessments of autonomy often focus on specific risks and rely on run-time evaluations -- observations of agent actions during operation. We introduce a code-based assessment of autonomy that eliminates the need to run an AI agent to perform specific tasks, thereby reducing the costs and risks associated with run-time evaluations. Using this code-based framework, the orchestration code used to run an AI agent can be scored according to a taxonomy that assesses attributes of autonomy: impact and oversight. We demonstrate this approach with the AutoGen framework and select applications.
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