Inference-Gated Adaptive Architecture: A Closed-Loop Systems Model for Adaptive Enterprise Execution

This paper introduces Inference-Gated Adaptive Architecture (IGAA), an architectural pattern for enterprise systems operating under uncertainty. In IGAA, operational entities are not records — they are belief states: continuously updated probabilistic representations derived from observed signals. Actions are selected by policies conditioned on belief. Execution passes through deterministic gates that enforce compliance, rate limits, and auditability. Outcomes feed back into belief, closing the loop. IGAA defines five structural requirements: perception-layer enrichment with minimum signal thresholds, belief-state entity modelling, closed-loop policy execution, deterministic execution gating, and replayable auditability. No widely-adopted enterprise architecture pattern unifies these requirements into a single doctrine. The paper formalises the architecture, specifies its mathematical components, and presents simulation results from a controlled evaluation across 20 seeds and 10,000 entities in a revenue execution domain. IGAA achieves a 60.7% higher positive engagement rate per contact action, 70.8% higher conversion efficiency, 70.6% fewer absolute negative outcomes, and zero compliance violations (a structural guarantee of the execution gate) relative to a fixed-cadence baseline, while improved adaptive baselines generate up to 17,760 violations across the same population.