Autonomous AI Decision Control Tower: A Deterministic Multi-Agent Decision Intelligence Framework for Enterprise NoOps

Abstract

The contemporary paradigm shift toward true “NoOps” environments necessitates an epistemological transition from traditional reactive monitoring to a regime of proactive, high-dimensional reasoning. This paper introduces the Autonomous AI Decision Control Tower (A-ADCT), an avantgarde framework designed to conceptualize enterprise operations as a stochastic, high-entropy state space governed by a set of competing multi-variate constraints. Unlike conventional AIOps platforms that focus on symptom identification, A-ADCT synthesizes a decentralized Multi-Agent System (MAS) with a Deep Reinforcement Learning (DRL) meta-orchestrator to resolve the inherent tension between performance utility, fiscal prudence, and security-compliance risk. We formalize this interaction as a Constrained Markov Decision Process (CMDP) and introduce a novel Consensus-weighted Scoring mechanism that projects agent-specific utility vectors onto a collective decision manifold. By decoupling semantic reasoning from deterministic execution, the framework mitigates the “Remediation Paradox”—where localized automated fixes trigger cascading systemic failures. Evaluation across a production-grade, cloud-native landing zone reveals a 28.3% reduction in Mean Time to Resolution (MTTR) versus the strongest autonomous-agent baseline (AIOpsLab; 198 s vs. 142 s)—and up to 87% versus legacy scripted remediation—a 99.9% policy compliance rate, and a 29% reduction in marginal operational costs versus legacy scripted remediation (7% versus AIOpsLab). A working prototype implementing this architecture demonstrates real-time anomaly-to-decision autonomy across distributed infrastructure states. This work demonstrates significant improvements for enterprise-scale decision intelligence, providing a scalable, hallucination-mitigated architecture for autonomous infrastructure management.