When automated decision systems fail, organizations frequently discover that formally compliant governance infrastructure cannot reconstruct what happened or why. This paper synthesizes an operational governance evidence framework -- structural accountability collapse diagnostics, decision trace schemas, evidence sufficiency measurement, and label-free monitoring -- into an integrated chain and analytically assesses its transferability across four decision system architectures. The cross-architecture comparison reveals a governance coverage gradient: deterministic rule engines achieve full DES-property fillability, hybrid ML+rules systems achieve partial fillability, classical ML systems achieve only minimal fillability, and agentic AI systems encounter structural breaks. We introduce the cascade of uncertainty, showing how governance failures propagate through serial dependencies between framework layers. For agentic systems, we identify three structural breaks -- decision diffusion, evidence fragmentation, and responsibility ambiguity -- and propose corresponding analytical extensions. Four propositions formalize the gradient, cascade compounding, delegation-depth effects, and extension sufficiency, establishing boundary conditions for the framework's valid operating envelope.

翻译：当自动化决策系统失效时，组织常常发现形式上合规的治理基础设施无法重构事件经过及其原因。本文构建了一个可操作的治理证据框架——包括结构性问责崩塌诊断、决策追踪模式、证据充分性度量及无标签监控——将其整合为一条连贯链条，并分析评估该框架在四种决策系统架构间的可迁移性。跨架构比较揭示了治理覆盖度的梯度规律：确定性规则引擎实现完全的可满足性证明属性填充，混合ML+规则系统实现部分填充，经典ML系统仅能达到最低限度填充，而智能体AI系统则遭遇结构性断裂。我们提出“不确定性级联”概念，揭示治理失效如何通过框架层间的串行依赖关系进行传播。针对智能体系统，我们识别出三种结构性断裂——决策分散化、证据碎片化与责任模糊化——并提出了相应的分析扩展方案。四项命题分别形式化了覆盖度梯度、级联效应累加、委托深度效应及扩展充分性，确立了框架有效运行包络的边界条件。