Abstract: The autonomous operation and continuous functioning capabilities of AI agents have transcended the operational boundaries of traditional generative AI, which centers on the "input-output" paradigm, and present new normative challenges to the existing "tool-control-responsibility" framework a framework predicated on stable human control. In terms of behavior identification, responsibility attribution, and operational regulation, the current legal system is insufficient to fully accommodate the cross-subject, multi interactive, and continuously operating characteristics of AI agents. Against this backdrop, this paper contends that AI agents should not be granted independent legal personality. Instead, it proposes a response grounded in the functional reconstruction of traditional static tool-oriented rules, while preserving the stability of the existing subject system. Adopting "functional instrumentalism" as the fundamental legal orientation for AI agents, this paper constructs a dual track identification framework "technological identity" and "legal attribution" to align behavioral identifiability with responsibility attributability under conditions of continuous operation. Furthermore, it introduces a "relationalist attribution" approach, which takes control relationships, interest structures, and risk sources as analytical dimensions to allocate responsibilities among multiple subjects in a structured manner. Employing the "principle of minimum necessity" as the boundary for institutional expansion, the paper thereby outlines an integrated governance framework that connects current law, special rules, and technical standards. Accordingly, the logic of AI governance is shifting from a static structure centered on behavioral outcomes and one off liability determinations toward a dynamic structure oriented around process of continuous operation.

Key words: AI agents; functional instrumentalism; identity recognition; relationalistic riability pathway; dynamic governance

摘要： AI智能体的自主行动与持续执行能力突破了传统生成式人工智能（Generative Artificial Intelligence，GAI）以“输入—输出”为中心的运行边界，对既有建立在主体稳定控制基础上的“工具—控制—责任”结构提出新的规范挑战。现行法律体系在行为识别、责任归属与运行控制等方面，已难以完全适应其跨主体、多交互与持续运行的行为特征。在此背景下，本文主张不赋予AI智能体独立法律人格，而是在维持既有主体制度稳定的前提下，通过对传统静态工具规则进行功能化重构予以回应。本文以“功能化工具主义”作为AI智能体的基本法律定位，构建“技术身份—法律归属”双轨识别结构，以实现持续运行条件下行为可识别性与责任可归属性的统一；进一步引入“关系主义归责”路径，以控制关系、利益结构与风险来源为分析维度，对多元主体责任进行结构化配置；并以“最小必要原则”作为制度扩展边界，形成“现行法律—专门规则—技术标准”相衔接的融合治理框架建议。由此，AI治理逻辑正在由围绕行为结果与一次性责任判断的静态结构，转向以持续运行过程为中心的动态治理结构。

关键词: AI智能体；功能化工具主义；身份识别；关系主义归责路径；动态治理