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或因果。本文综合了一个操作性的治理证据框架——包括结构性问责崩溃诊断、决策追踪框架、证据充分性度量以及无标注监控——将其整合为一条完整链路，并跨四种决策系统架构进行可迁移性分析。跨架构比较揭示了一个治理覆盖梯度：确定性规则引擎实现完全的DES属性可填充性，混合ML+规则系统实现部分可填充性，经典ML系统仅实现最低可填充性，而智能体AI系统遭遇结构性断裂。我们引入不确定性级联概念，展示治理失效如何通过框架层之间的串行依赖性传播。针对智能体系统，我们识别出三项结构性断裂——决策扩散、证据碎片化和责任模糊性——并提出相应的分析性扩展。四项命题形式化地阐述了梯度、级联复合效应、委派深度效应及扩展充分性，确立了该框架有效运行范围的边界条件。