Policy-Verified Agentic DataOps for Regulated Multi-Cloud Analytics

Regulated analytics teams increasingly run data pipelines, retrieval systems, and machine learning workloads across multiple cloud providers, but the operational control plane remains split across pipeline schedulers, API gateways, privacy tooling, and audit systems. This paper proposes Policy-Verified Agentic DataOps (PVA-DataOps), a synthetic reference architecture in which bounded agents propose operational actions while a policy verifier, evidence layer, and contract-mediated gateway decide whether those actions may execute. The design combines governed cloud pipeline agents, intent-aware multi-cluster gateways, cross-cloud workload routing, hybrid semantic-relational retrieval, distributed retrieval-augmented generation (RAG), model-context contracts, and privacy-preserving anonymization. We describe the control architecture, a verification algorithm, and a simulated benchmark covering recovery time, policy drift, evidence validity, privacy-utility loss, and cost. In simulation, PVA-DataOps reduces mean recovery time by 46.3% relative to static orchestration while eliminating unapproved policy-drift events. The results are synthetic and are intended to make the architecture testable rather than to claim production deployment.