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

• URL: http://arxiv.org/abs/2605.04093v1
• Date: Wed, 29 Apr 2026 09:59:36 GMT
• Title: Decision Evidence Maturity Model for Agentic AI: A Property-Level Method Specification
• Abstract summary: Agentic AI systems produce decision evidence at scale through execution telemetry, but property-level reconstruction often fails.<n>We name this pattern the container fallacy: the automatic equation of evidence-container presence with audit sufficiency.<n>This paper specifies the Decision Evidence Maturity Model (DEMM), a property-level reconstructability method for agentic decisions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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