Decision Evidence Maturity Model for Agentic AI: A Property-Level Method Specification

TL;DR

This paper introduces the Decision Evidence Maturity Model (DEMM), a property-level method to improve reconstructability of decision evidence in agentic AI systems, addressing the container fallacy.

Contribution

It specifies a new property-level reconstructability method, classifies evidence sufficiency into categories, and provides an open-source tool for evidence aggregation.

Findings

Feasibility exercise on 140 synthetic scenarios and 3 public incidents.

Completeness range of evidence reconstruction was 53.6% to 100%.

The approach is implementation-dependent, not externally validated.

Abstract

Agentic AI systems produce decision evidence at scale through execution telemetry, but property-level reconstruction often fails when an external party asks a specific governance question about a specific decision: the assembled evidence is insufficient to answer it. We name this pattern the container fallacy: the automatic equation of evidence-container presence with audit sufficiency. This paper specifies the Decision Evidence Maturity Model (DEMM), a property-level reconstructability method for agentic decisions. DEMM classifies evidence sufficiency into four executable categories plus a protocol-level "conflicting" category and aggregates per-property verdicts into a five-level capability rubric anchored to the established maturity-model lineage. The open-source Decision Trace Reconstructor ships ten executable adapter-fallback classes spanning vendor SDKs, protocol traces, public-postmortem prose, and generic JSONL records. A reproducible feasibility exercise runs the protocol on 140 synthetic scenarios plus three public incidents; the resulting completeness range (53.6% to 100%) is implementation behaviour, not external validation.