From DevOps to XOps: an agent-driven reference architecture for autonomous enterprise operations
Enterprise adoption of machine learning has fragmented operations into specialised disciplines—DataOps, MLOps, AIOps—creating silos that impede unified governance. We propose XOps, a five-layer reference architecture integrating PlatformOps, DataOps, MLOps and AIOps beneath an Agentic Orchestration layer with Policy-as-Code governance, together with a continuous-time Markov chain model quantifying the availability effect of agent-driven remediation. Two case studies evaluate the architecture under controlled, synthetic conditions. For a self-healing payment gateway, 250 live executions of the reasoning graph against a hosted language model and a live policy engine yield 85.6% plan-level action consistency and 99.6% fault classification accuracy, with 12.4% of plans rejected by the policy gate and escalated to a human and no policy-violating action authorised for execution; the pipeline’s 3.3-minute recovery time is a simulated latency budget rather than a cluster measurement. For a predictive-maintenance application on NASA C-MAPSS data, autonomous drift detection and retraining sustain $$R^2 = 0.74$$ against 0.29 for an equivalent static model, measured on engines reserved entirely from retraining. An indicative cost analysis suggests an approximately 70% reduction in expected monthly operational cost. Within this scope the results support the feasibility of agent-driven operations rather than establishing production-scale performance.