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.

翻译：主体性人工智能正迅速渗透到软件工程等多样化的实际领域，但公众信任度并未同步提升。核心原因在于，尽管责任问题被广泛讨论，它仍是一个主观且未得到强制落实的概念——因为当前没有哪种主体性框架能在组合系统（非任何单一主体设计）产生危害时，生成可量化、可追溯、可干预的溯源信息以分配责任。我们认为，缺失的并非更优的基准测试评估，而是贯穿整个主体生命周期中的**显式溯源**，这是使责任可计算、可实施的唯一可行基础。我们沿四个维度推进该议程：通过识别社会技术维度中的责任缺口，确立*为何*这种溯源是结构性必需品；通过因果归因函数和责任张量，形式化*需编码什么*内容；通过跨四个生命周期层讨论*如何*使其可计算（初步实验表明，在不可逆危害累积前，溯源可在线估计与干预）；通过具体的主体事件审视*谁*应承担责任。显式溯源并非可选的改良措施，而是负责任主体性AI的必要条件，生态系统中任何利益相关者都不可将其视为可有可无。