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This paper synthesizes an operational governance evidence framework, including structural accountability diagnostics and decision trace schemas, to assess decision system architectures. The analysis reveals a governance coverage gradient across deterministic rule engines, hybrid ML+rules, classical ML, and agentic AI systems, with agentic systems exhibiting structural breaks like decision diffusion. The authors introduce the "cascade of uncertainty" to explain how governance failures propagate and propose analytical extensions to address the identified structural breaks in agentic systems.
Agentic AI systems introduce fundamental breaks in governance frameworks, making it difficult to reconstruct what happened or why decisions were made.
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.
