Articles Open Access
Development of Multi-Agent Decision Frameworks for Autonomous Cloud DevOps and Software Delivery Optimization
Senior Software Engineer, Oslo, Norway
Abstract
Modern cloud-native software engineering demands rapid feature delivery alongside strict operational reliability, stretching traditional Continuous Integration and Continuous Deployment (CI/CD) pipelines to their technical limits. Traditional automation relies heavily on static, human-engineered scripting that fails to adapt to non-deterministic runtimes, configuration drifts, or sudden infrastructure anomalies. This paper presents a comprehensive, next-generation Multi-Agent Decision Framework designed to achieve end-to-end autonomy in cloud DevOps and software delivery optimization. By distributing cognitive and operational workloads among specialized, role-based artificial intelligence agents—encompassing architectural validation, source-code analysis, automated test orchestration, cost-optimized Infrastructure as Code (IaC) provisioning, and self-healing Site Reliability Engineering (SRE)—the framework shifts software delivery from manual workflows to a dynamic, collaborative ecosystem. We formalize a consensus-driven decision engine anchored on confidence scoring, dynamic Directed Acyclic Graph (DAG) task routing, and continuous reinforcement learning loops fed by real-time observability telemetry. Empirical validations demonstrate that the proposed multi-agent deployment significantly minimizes change failure rates, optimizes cloud compute expenditures, and shrinks the mean time to resolution (MTTR) for infrastructure incidents. Ultimately, this research provides a scalable paradigm for executing intelligent, zero-touch autonomous DevOps pipelines within secure, multi-cloud enterprise production architectures.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 5 |
| Pages | 17541-17548 |
| Published | 2025-10-10 |
| Issue | Vol. 9 No. 5 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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27. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
28. Soundappan, S. J. (2023). AI-Driven Secure Enterprise Analytics and Intelligent Cloud Data Management Frameworks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(3), 8236-8242.
29. Adabala, P. K. (2024). Utilizing predictive analytics to improve efficiency and decisionmaking in ERP-connected supply chains. International Journal of Intelligent Systems and Applications in Engineering, 12, 2465.
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33. Rahman, M. S., Siddiqui, M. I. H., Rashid, S. U., Kabir, A. A., Mahmud, F. U., & Shammah, R. S. (2024). Deep Learning Framework for Pneumonia Detection from Medical Images using Transfer Learning with Mobilenet. Journal of Adaptive Learning Technologies, 1(11), 12-22.
2. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
3. Rajula, A. (2023). Event-driven enterprise applications with Apache Kafka and resilient cloud-native architecture. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9063–9073.
4. Gopisetty, S. (2025). Keeping Watch Without Breaking Trust: Designing Observability That Speaks Both to Engineers and Regulators. International Journal of Emerging Trends in Computer Science and Information Technology, 6(1), 184-190.
5. Kotla, Mutha Ravi Tej (2022). Intelligent cloud-native banking: Leveraging machine learning for secure and scalable digital financial services. International Journal of Engineering and Technology Research, 7(1), 58–81. https://doi.org/10.34218/IJETR_07_01_005
6. Soundappan, S. J. (2022). Integrated Risk Governance Framework for Financial Compliance Supply Chain Resilience and Enterprise Data Management. International Journal of Computer Technology and Electronics Communication, 5(6), 16254-16263.
7. Raja, G. V. (2023). Modernizing enterprise systems using AI with machine learning and cloud computing for intelligent systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
8. Meesala, L. K. (2024). Converging Infrastructure and Security: A Maturity-Based Approach to Cloud-Native Data Protection, SIEM Optimization, and Compliance Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 283-289.
9. Mathew, A., & Alex, H. (2023). From Code to Cure: The Role of AI in Accelerating Drug Discovery. Advances and Challenges in Science and Technology Vol. 2, 94-102.
10. Chukkala, R. (2025, April). The Convergence of CCAI, Chatbots, and RCS Messaging: Redefining Business Communication in the AI Era. In International Conference of Global Innovations and Solutions (pp. 194-213). Cham: Springer Nature Switzerland.
11. Seetala, S. R. (2016). Strategic architecture patterns and design principles for enterprise-grade data integration in large-scale, multi-source and distributed platform environments. European Journal of Advances in Engineering and Technology, 3(8), 125-135.
12. Goel, N. (2023). Implementing secure access controls in computer security frameworks. Communications on Applied Nonlinear Analysis, 30(4), 289–301.
13. Vasa, M. R. (2024). AI-Augmented Fund Accounting Repository for Governance-Driven Billing Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 250-257.
14. Vollem, S. (2022). Streaming-First Enterprise Decision Systems: Architectural Evolution from Batch Dataflows to Stateful, Exactly-Once Real-Time Processing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4326-4335.
15. Raja, G. V., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. The International Journal of Research Publications in Engineering, Technology and Management, 5(4), 7152–7162.
16. Narayanan, S. (2024). Enterprise technology risk management framework: An integrated approach to cloud-native security, AI governance, and compliance automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(1), 421–434. https://doi.org/10.32628/CSEIT2612126
17. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
18. Sojol, J. I., Alam, M. S., Hossain, N., & Motahar, T. (2019, February). Smart School Bus: Ensuring Safety On The Road For School Going Children. In 2019 21st International Conference on Advanced Communication Technology (ICACT) (pp. 734-739). IEEE.
19. Meesala, A. (2024). Machine Learning Enabled Governance Framework for Autonomous Enterprise Platforms and Intelligent Data Ecosystems. International Journal of Computer Technology and Electronics Communication, 7(6), 9899-9909.
20. Kale, P. (2024). A Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making in Software Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 274-282.
21. Soni, H., & Ramamurthy, P. (2024). Operationalizing generative AI architectures: LLMOps, retrieval-augmented systems, and real-time enterprise integration. International Journal of Research and Applied Innovations (IJRAI), 7(3), 10807–10811.
22. Veershetty. (2022). Digital modernization of gas utility operations: Architecture, scaled-agile delivery, and assurance. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7791–7796.
23. Gunda, S. R. (2025). Optimizing Cloud Computing Resource Utilization Through Intelligent Allocation and Containerization Strategies. Journal of Computer Science and Technology Studies, 7(8), 238-244.
24. Begum, R. S., & Sugumar, R. (2016). Conditional entropy with swarm optimization approach for privacy preservation of datasets in cloud [J]. Indian Journal of Science and Technology, 9(28).
25. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
26. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
27. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
28. Soundappan, S. J. (2023). AI-Driven Secure Enterprise Analytics and Intelligent Cloud Data Management Frameworks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(3), 8236-8242.
29. Adabala, P. K. (2024). Utilizing predictive analytics to improve efficiency and decisionmaking in ERP-connected supply chains. International Journal of Intelligent Systems and Applications in Engineering, 12, 2465.
30. Mohammed, S. (2022). Designing secure zero trust architectures for global enterprise environments. International Journal of Science, Research and Technology (IJSRAT), 5(6), 8953–8956.
31. Mathew A R, Al Zahli J A. Cloud Technology and the Challenges for Forensics InvestigatorsJ. DEStech Transactions on Computer Science and Engineering, 2017 (cnsce).
32. Gujarathi, M. (2025). Human-in-the-loop control patterns in automated enterprise workflows. International Journal of Future Innovative Science and Technology (IJFIST), 8(1), 14176–14185.
33. Rahman, M. S., Siddiqui, M. I. H., Rashid, S. U., Kabir, A. A., Mahmud, F. U., & Shammah, R. S. (2024). Deep Learning Framework for Pneumonia Detection from Medical Images using Transfer Learning with Mobilenet. Journal of Adaptive Learning Technologies, 1(11), 12-22.
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