Agent-Based Artificial Intelligence Models for Enterprise Cloud Governance and Resilience
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P131Keywords:
Agent-Based Artificial Intelligence, Enterprise Cloud Governance, Cloud Resilience, Autonomous Systems, Multi-Agent Systems, Intelligent Cloud Security, Predictive Analytics, Self-Healing Infrastructure, Cloud Automation, AI GovernanceAbstract
The rapid evolution of enterprise cloud ecosystems has significantly transformed organizational digital infrastructures by enabling scalable computing, distributed services, and dynamic operational management. However, the increasing complexity of cloud-native environments has also introduced critical challenges related to governance, cybersecurity, infrastructure resilience, compliance management, workload orchestration, and fault recovery. Traditional governance mechanisms often rely on static rule-based systems that lack adaptive intelligence and autonomous response capabilities. In this context, Agent-Based Artificial Intelligence (ABAI) models have emerged as a transformative paradigm for intelligent cloud governance and resilient infrastructure management. This research article investigates the role of agent-based AI architectures in strengthening enterprise cloud governance frameworks while enhancing operational resilience across distributed cloud ecosystems. The study explores how autonomous AI agents can perform real-time monitoring, predictive analytics, policy enforcement, anomaly detection, adaptive orchestration, and self-healing operations within enterprise cloud environments. The article further evaluates the integration of multi-agent systems with cloud security, compliance automation, and service continuity management. A comparative assessment between traditional governance models and agent-driven intelligent governance architectures is also presented to highlight operational improvements. The research methodology employs a conceptual analytical framework supported by secondary data analysis, literature synthesis, and comparative evaluation models. The findings indicate that agent-based AI systems substantially improve governance efficiency, infrastructure reliability, cyber resilience, and decision automation while reducing operational latency and administrative overhead. The study concludes that ABAI-driven governance frameworks represent the future of intelligent enterprise cloud operations and resilient digital transformation strategies.
References
1. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.
2. Kaidhapuram, S. R. (2023). Composable architecture for enterprises: Principles, adoption patterns, and strategic impact. International Journal of Computer Techniques (IJCT), 10(4), 1–6. https://ijctjournal.org/composable-architecture-enterprises/
3. Arora, A. S., Yachamaneni, T., & Kotadiya, U. (2021). Optimizing Multi-Tenant Resource Allocation in Cloud-Based Distributed Systems for Large-Scale AI Model Training Using In-Memory Computing. International Journal of Emerging Research in Engineering and Technology, 2(1), 37-46.
4. Nalluri, S., Kaidhapuram, S. R., Alkhuzaie, A. A. A., S, S. K., & Sofia Liz, D. R. A. (2025). Comprehensive analysis on security challenges in virtualized cloud infrastructure. In 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) (pp. 1–6). Bengaluru, India. IEEE. https://doi.org/10.1109/ICICKE65317.2025.11136769
5. Wooldridge, M. (2021). An Introduction to MultiAgent Systems. Wiley Publications.
6. Zhang, H., Lee, K., & Sharma, P. (2022). Intelligent multi-agent systems for enterprise cloud resilience. Journal of Cloud Computing, 11(4), 115–132.
7. Janardhanan, H. Ethics, Governance, and Fairness in Large-Scale Data Science and Algorithmic Decision Systems.
8. Kaidhapuram, S. R. (2024). Zero ETL integration and data fabric for analytics warehouses. International Journal of Computer Science Engineering Techniques (IJCSE), 8(5), 1–12. https://www.ijcsejournal.org/zero-etl-integration-data-fabric/
9. Kumar, R., & Patel, S. (2023). AI-driven predictive orchestration in hybrid cloud infrastructures. International Journal of Distributed Systems, 18(2), 77–96.
10. Li, F. F. (2022). Context-aware AI systems for adaptive cloud governance. IEEE Transactions on Cloud Computing, 10(5), 2211–2224.
11. Seknametla, P. R. (2026). Advanced Telemetry Correlation Techniques for Real-Time Reliability Engineering in Edge-Cloud Systems. International Journal of Science, Technology and Convergence, 8(8). Retrieved from https://ijcdra.us/index.php/IJSTC/article/view/67
12. IBM Corporation. (2023). AI and Autonomous Cloud Operations Framework. IBM Research Publications.
13. Gartner Research. (2024). Future of Intelligent Cloud Governance and Resilience Engineering. Gartner Technical Reports.
14. Amazon Web Services. (2023). Cloud Governance Best Practices and AI Automation. AWS Whitepaper Series.
15. SUNKARA, S. K. (2025). LEVERAGING AI, IoT, AND BLOCKCHAIN FOR SCALABLE DIGITAL TRANSFORMATION IN POST-HARVEST SUPPLY CHAINS: A MULTI-SECTOR APPROACH TO ENHANCING EFFICIENCY AND TRACEABILITY (Vol. 26, Issue 7, pp. 2757–2766).
16. Kaidhapuram, S. R., Al-Akayshee, A. S., D, A., Seknametla, P. R., & M, D. (2025). Temporal convolution network with long short-term memory based predictive diagnosis for personalized healthcare. In 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) (pp. 1–6). Bengaluru, India. IEEE. https://doi.org/10.1109/ICICKE65317.2025.11136460
17. Microsoft Azure. (2024). AI-Based Enterprise Cloud Security and Governance Architecture. Microsoft Technical Documentation.
18. Gajula, S., & Kandula, S. T. R. (2025). Through AI, blockchain, and attribute-based encryption for secure cloud financial infrastructure. In Proceedings of Fifth International Conference on Computing and Communication Networks: ICCCN 2025 (Vol. 6, p. 397). Springer Nature. https://books.google.co.in/books?id=lx3aEQAAQBAJ
19. Chen, L., & Rao, M. (2023). Self-healing infrastructure models using autonomous AI agents. International Journal of Artificial Intelligence Research, 15(1), 55–74.
20. Kotadiya, U., Yachamaneni, T., & Arora, A. S. (2025, August). Block Chain Audited Homomorphic Encryption for Consortium Credit Risk Modelling. In International Conference on Computing and Communication Networks (pp. 410-433). Cham: Springer Nature Switzerland.
21. Singh, A., & Verma, D. (2022). Adaptive cloud governance using reinforcement learning agents. Journal of Enterprise Computing, 9(3), 140–158.
22. Kaidhapuram, S. R. (2020). Microservices architecture and real-time streaming for pharmaceutical use-cases. International Journal of Computer Science Engineering Techniques (IJCSE), 4(3), 1–8. https://www.ijcsejournal.org/microservices-architecture-streaming-pharmaceutical/
23. S. J. Bodapati and S. Merakanapalli, "Unified Wire-Control Chassis System for Software-Defined Vehicles," 2026 5th International Conference on Communication, Computing and Electronics Systems (ICCCES), Coimbatore, India, 2026, pp. 01-08, doi: 10.1109/ICCCES62661.2026.11437259.
24. Ahmed, T., & Wilson, R. (2023). Predictive cyber resilience using agent-based AI frameworks. Cybersecurity Engineering Review, 6(2), 88–104.
25. Seknametla, P. R. (2023). Automated Root Cause Analysis in Microservice Architectures: Leveraging Distributed Trace Correlation with OpenTelemetry for Faster Incident Resolution. International Journal of Emerging Research in Engineering and Technology, 4(1), 158-164. https://doi.org/10.63282/3050-922X.IJERET-V4I1P117
26. NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.
27. A Review of Anomaly Identification in Finance Frauds using Machine Learning System. (2023). International Journal of Current Engineering and Technology, 13(6), 568-575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820
28. European Commission. (2024). Ethical Guidelines for Trustworthy Artificial Intelligence. European Union Publications.
29. Sreenivasulu Gajula. (2025). Cybersecurity in SCM Role of IAM, Zero Trust, and Blockchain. Asian Journal of Computer Science Engineering(AJCSE), 10(2). https://doi.org/10.22377/ajcse.v10i2.220
