智能体AI开发者终于有了责任归属的量化框架——本文提出的显式溯源机制解决了“AI出错谁负责”的核心难题，做AI安全、合规和系统治理的团队值得深入研究。
本文指出智能体AI在软件工程等领域快速普及，但公众信任滞后，核心原因是缺乏可量化、可追溯、可干预的显式溯源机制，导致责任无法分配。作者认为当前缺失的不是更好的基准评估，而是贯穿智能体全生命周期的显式溯源，这是让责任变得可计算和可操作的唯一基础。论文从四个维度推进：通过识别社会技术维度的责任缺口说明溯源的必要性，通过因果归因函数和责任张量形式化定义溯源内容，通过四层生命周期实验证明溯源可在线估计和干预，并通过具体智能体事件讨论责任归属。显式溯源不是可选的改进，而是负责任智能体AI的必要条件。
Responsible Agentic AI Requires Explicit Provenance
Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reason is that responsibility, despite being widely discussed, remains a subjective and unenforced concept, as no current agentic framework produces the quantifiable, traceable, and interventionable provenance needed to assign it when harm emerges from compositions no single party designed. We position that what is missing is not better benchmark-level evaluation but $\textbf{explicit provenance}$ across the full agentic lifecycle, which is the only viable basis for making responsibility computable and actionable. We advance this agenda along four axes: establishing $\textit{why}$ such provenance is a structural necessity by identifying responsibility gaps across sociotechnical dimensions, formalizing $\textit{what}$ it must encode through a causal attribution function and responsibility tensor, discussing $\textit{how}$ it can be made computable across four lifecycle layers with preliminary experiments showing that provenance is estimable and interveneable online before irreversible harm accumulates, and examining $\textit{who}$ bears responsibility through a concrete agentic incident. Explicit provenance is not a discretionary refinement but the necessary condition for responsible agentic AI, and no stakeholder across its ecosystem can afford to treat it as optional.