Governed Auditable Decisioning Under Uncertainty: Synthesis and Agentic Extension

TL;DR

This paper analyzes governance frameworks for automated decision systems, identifying limitations across architectures and proposing extensions for agentic AI to improve accountability and traceability.

Contribution

It synthesizes an operational governance framework, assesses its transferability across architectures, and introduces analytical extensions for agentic AI systems.

Findings

Deterministic rule engines achieve full governance coverage.

Hybrid ML+rules systems achieve partial coverage.

Agentic AI systems face structural breaks in governance.

Abstract

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.