Autonomous Database Administration Using Generative AI and Retrieval-Augmented Generation for Intelligent Enterprise Cloud Systems

Abstract

Enterprise database environments have grown into sprawling, heterogeneous estates spanning on-premises systems, Oracle Cloud Infrastructure, Amazon Web Services, and Microsoft Azure, placing an unsustainable operational burden on human database administrators (DBAs). Traditional automation, rooted in rule-based monitoring and narrow machine learning models, addresses isolated tasks such as index tuning or anomaly scoring, but it cannot reason across the full administrative lifecycle, nor explain its decisions in terms a human operator can trust and act upon. This paper introduces the Artificial Intelligence Driven Autonomous Database Administration System (AIDAS), a novel conceptual framework that unifies generative artificial intelligence, retrieval-augmented generation (RAG), vector search, multi-agent large language model (LLM) orchestration, and the Model Context Protocol (MCP) into a single governed architecture for autonomous enterprise database administration. AIDAS is organized as a seven-layer pipeline spanning telemetry ingestion, enterprise knowledge retrieval, standardized tool access, multi-agent reasoning, governed execution, and continuous institutional-memory capture. Unlike prior LLM-for-database systems, which typically target a single task such as diagnosis or query rewriting, AIDAS is architected to intelligently automate monitoring, incident detection, root cause analysis, infrastructure troubleshooting, performance and query optimization, security monitoring, compliance validation, automated documentation, and decision support as a coherent whole, while remaining portable across heterogeneous cloud database platforms through an MCP-based integration fabric. The paper presents the system architecture, the enterprise RAG pipeline, the multi-agent decision workflow, a formal orchestration algorithm, an implementation strategy, and four enterprise use cases. Because AIDAS is presented as a design-science contribution rather than a deployed and empirically measured system, the performance discussion is framed conceptually against patterns reported in the cited AIOps and LLM-for-database literature, and empirical validation is explicitly identified as future work. Six original contributions are articulated and differentiated from existing enterprise AI database solutions, and the paper concludes with a candid discussion of limitations, including hallucination risk, MCP security exposure, and the governance requirements of autonomous change execution in production environments.