Governing Cloud Data Pipelines with Agentic AI
Aswathnarayan Muthukrishnan Kirubakaran, Adithya Parthasarathy, Nitin Saksena, Ram Sekhar Bodala, Akshay Deshpande, Suhas Malempati, Shiva Carimireddy, Abhirup Mazumder
TL;DR
Cloud data pipelines face dynamic workloads, evolving schemas, cost constraints, and strict governance requirements. The authors present Agentic Cloud Data Engineering, a policy-aware control-plane architecture that embeds bounded AI agents to reason over telemetry, metadata, and declarative governance policies to propose constrained operational actions rather than directly executing changes. The architecture organizes three planes—the Data Plane, the Policy and Governance Plane, and the Agentic Control Plane—with specialized agents for monitoring, optimization, schema drift handling, and recovery; all agent proposals are validated by governance before execution. In experiments using batch and streaming workloads, the platform achieves up to $45\%$ reductions in mean pipeline recovery time, about $25\%$ lower operational cost, and over $70\%$ fewer manual interventions, while maintaining data freshness and policy compliance.
Abstract
Cloud data pipelines increasingly operate under dynamic workloads, evolving schemas, cost constraints, and strict governance requirements. Despite advances in cloud-native orchestration frameworks, most production pipelines rely on static configurations and reactive operational practices, resulting in prolonged recovery times, inefficient resource utilization, and high manual overhead. This paper presents Agentic Cloud Data Engineering, a policy-aware control architecture that integrates bounded AI agents into the governance and control plane of cloud data pipelines. In Agentic Cloud Data Engineering platform, specialized agents analyze pipeline telemetry and metadata, reason over declarative cost and compliance policies, and propose constrained operational actions such as adaptive resource reconfiguration, schema reconciliation, and automated failure recovery. All agent actions are validated against governance policies to ensure predictable and auditable behavior. We evaluate Agentic Cloud Data Engineering platform using representative batch and streaming analytics workloads constructed from public enterprise-style datasets. Experimental results show that Agentic Cloud Data Engineering platform reduces mean pipeline recovery time by up to 45%, lowers operational cost by approximately 25%, and decreases manual intervention events by over 70% compared to static orchestration, while maintaining data freshness and policy compliance. These results demonstrate that policy-bounded agentic control provides an effective and practical approach for governing cloud data pipelines in enterprise environments.