Authors : Jipsa Mathew; Thomas Philip

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/yd66v3h9

DOI : https://doi.org/10.38124/ijisrt/26jul1270

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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.

Keywords : Autonomous Database Administration; Generative Artificial Intelligence; Retrieval-Augmented Generation; Large Language Models; Model Context Protocol; Multi-Agent Systems; AIOps; Cloud Database Management; Vector Search; Enterprise Knowledge Base.

References :

1. Amazon Web Services. (2024). Amazon Aurora machine learning-based performance insights and optimization. AWS Documentation.
2. Anthropic. (2024). Introducing the Model Context Protocol. Anthropic News. https://www.anthropic.com/news/model-context-protocol
3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
4. Descope. (2026). What is the Model Context Protocol (MCP) and how it works. Descope Security Research.
5. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
6. Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., & Larson, J. (2024). From local to global: A graph RAG approach to query-focused summarization. arXiv:2404.16130.
7. European Parliament and Council. (2016). Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data (General Data Protection Regulation). Official Journal of the European Union.
8. European Parliament and Council. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (AI Act). Official Journal of the European Union.
9. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Guo, Q., Wang, M., & Wang, H. (2024). Retrieval-augmented generation for large language models: A survey. arXiv:2312.10997.
10. Guo, Z., Xia, L., Yu, Y., Ao, T., & Huang, C. (2024). LightRAG: Simple and fast retrieval-augmented generation. arXiv preprint.
11. Helskyaho, H., Kraft, J., & Koning, J. (2021). Oracle Autonomous Database for machine learning. Oracle machine learning research reports.
12. Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., & Wu, C. (2023). MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv:2308.00352.
13. Hu, S., Lu, C., & Clune, J. (2024). Automated design of agentic systems. arXiv:2408.08435.
14. Johnson, J., Douze, M., & Jégou, H. (2021). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535–547.
15. Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W. (2020). Dense passage retrieval for open-domain question answering. Proceedings of EMNLP 2020, 6769–6781.
16. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
17. Li, G., Zhou, X., Li, S., & Gao, B. (2019). QTune: A query-aware database tuning system with deep reinforcement learning. Proceedings of the VLDB Endowment, 12(12), 2118–2130.
18. Li, X., et al. (2024). A survey on LLM-based multi-agent systems: Workflow, infrastructure, and challenges. Vicinagearth, 1, 9.
19. Microsoft Azure. (2024). Azure SQL Database automatic tuning documentation. Microsoft Learn.
20. National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (NIST AI 100-1). U.S. Department of Commerce.
21. Ong, I., Almahairi, A., Wu, V., Chiang, W. L., Wu, T., Gonzalez, J. E., Kadous, M. W., & Stoica, I. (2025). RouteLLM: Learning to route LLMs from preference data. International Conference on Learning Representations.
22. Oracle Corporation. (2023). Self-driving database: Autonomous Database Oracle 19c. Oracle Product Documentation.
23. Ouyang, B., Zhang, Y., et al. (2025). RCRank: Multimodal ranking of root causes of slow queries in cloud database systems. Proceedings of the ACM on Management of Data.
24. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
25. Packowski, S., Halilovic, I., Schlotfeldt, J., & Smith, T. (2024). Optimizing and evaluating enterprise retrieval-augmented generation (RAG): A content design perspective. arXiv:2410.12812.
26. Pavlo, A., Butrovich, M., Joshi, A., Ma, L., Menon, P., Van Aken, D., Lee, L., & Salakhutdinov, R. (2021). Make your database system dream of electric sheep: Towards self-driving operation. Proceedings of the VLDB Endowment, 14(12), 3211–3221.
27. Prasad, P., & Rich, C. (2018). Innovation insight for AIOps. Gartner Research.
28. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of EMNLP-IJCNLP 2019, 3982–3992.
29. Roy, D., Zhang, X., Bhave, R., Bansal, C., Las-Casas, P., Fonseca, R., & Rajmohan, S. (2024). Exploring LLM-based agents for root cause analysis. arXiv:2403.04123.
30. Sarthi, P., Abdullah, S., Tuli, A., Khanna, S., Goldie, A., & Manning, C. D. (2024). RAPTOR: Recursive abstractive processing for tree-organized retrieval. arXiv:2401.18059.
31. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36.
32. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
33. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35.
34. Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. arXiv:2308.08155.
35. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations.
36. Zhang, L., et al. (2025). A survey of AIOps in the era of large language models. arXiv:2507.12472.
37. Zhao, X., Zhou, X., & Li, G. (2024). Chat2Data: An interactive data analysis system with RAG, vector databases and LLMs. Proceedings of the VLDB Endowment.
38. Zhou, X., Chai, C., Li, G., & Sun, J. (2022). Database meets artificial intelligence: A survey. IEEE Transactions on Knowledge and Data Engineering, 34(3), 1096–1116.
39. Zhou, X., Jin, L., Sun, J., Zhao, X., Yu, X., Li, S., Wang, T., Li, K., & Liu, L. (2021). DBMind: A self-driving platform in openGauss. Proceedings of the VLDB Endowment, 14(12), 2743–2746.
40. Zhou, X., Li, G., Chai, C., & Feng, J. (2021). A learned query rewrite system using Monte Carlo tree search. Proceedings of the VLDB Endowment, 15(1), 46–58.
41. Zhou, X., Li, G., & Liu, Z. (2023). LLM as DBA. arXiv:2308.05481.
42. Zhou, X., Li, G., Sun, Z., Liu, Z., Chen, W., Wu, J., Liu, J., Feng, R., & Zeng, G. (2024). D-Bot: Database diagnosis system using large language models. Proceedings of the VLDB Endowment, 17(10), 2514–2527.
43. Zhou, X., Li, G., Wu, J., Liu, J., Sun, Z., & Zhang, X. (2023). A learned query rewrite system. Proceedings of the VLDB Endowment, 16(12), 4110–4113.
44. Zhou, W., Sun, J., Zhou, X., Li, G., Liu, L., Wu, H., & Wang, T. (2025). GaussMaster: An LLM-based database copilot system. arXiv:2506.23322.
45. Zhu, J. P., Cai, P., Niu, B., Ni, Z., Xu, K., Huang, J., Wan, J., Ma, S., Wang, B., Zhang, D., et al. (2024). AutoTQA: Towards autonomous tabular question answering through multi-agent large language models. Proceedings of ICDE 2024.
46. Zhuge, M., Wang, W., Kirsch, L., et al. (2024). GPTSwarm: Language agents as optimizable graphs. Proceedings of the International Conference on Machine Learning (ICML).