From Agile DevOps to AIOps: Transforming Infrastructure Operations Through AI-Driven Automation
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Abstract
The paper discusses how Artificial Intelligence in the IT Operations (AIOps) can be used to alter the classic approach to DevOps by incorporating the additional aspects of automation, prediction, and self-healing. The research methodology adopted is quantitative because it measures the change in incident reduction, response time and system efficiency following the incorporation of AIOps tools. It has been found that AIOps helps to decrease the number of operational incidents by over 60 per cent and increase a faster recovery speed. The models developed based on Python were used in anomaly detection and predictive analysis. It has been confirmed that AIOps can provide smarter, faster, and more reliable IT processes, and it can give the chance to take the right direction towards autonomous infrastructure management.
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[1] Oye, E., R, V., & Ladoke Akintola University of Technology. (2025). The Evolution of DevOps to AIOps: A Conceptual Framework for Intelligent Automation. The Evolution of DevOps to AIOps: A Conceptual Framework for Intelligent Automation. https://www.researchgate.net/publication/392663355_The_Evolution_of_DevOps_to_AIOps_A_Conceptual_Framework_for_Intelligent_Automation
[2] Mansour, I. J. S., Rejab, M. B. M., & Mahdin, H. B. (2024). Review in adoption of DevOps, AIOPs, DataOps, GITOps, MLOPs in IT MLEs in Germany. International Journal of Engineering Trends and Technology, 72(12), 64–76. https://doi.org/10.14445/22315381/ijett-v72i12p106
[3] Goli, A. K. R., Independent Researcher, & Site Reliability Engineer. (2024). Future-Proofing Software Development with AI-Driven DevOps Pipelines and AIOps. Future-Proofing Software Development With AI-Driven DevOps Pipelines and AIOps. https://powertechjournal.com
[4] Zhang, L., Jia, T., Jia, M., Wu, Y., Liu, A., Yang, Y., Wu, Z., Hu, X., Yu, P., & Li, Y. (2025). A survey of AIOPs in the era of Large Language Models. ACM Computing Surveys, 58(2), 1–35. https://doi.org/10.1145/3746635
[5] Eramo, R., Said, B., Oriol, M., Bruneliere, H., & Morales, S. (2024). An architecture for model-based and intelligent automation in DevOps. Journal of Systems and Software, 217, 112180. https://doi.org/10.1016/j.jss.2024.112180
[6] Thota, R. C. (2021). AI driven infrastructure automation-enhancing cloud efficiency with MLOps and DevOps. Global Journal of Engineering and Technology Advances, 8(3), 101–108. https://doi.org/10.30574/gjeta.2021.8.3.0140
[7] Joy, M., Venkataramanan, S., Ahmed, M., Mark, M., Gudala, L., Shaik, M., Venkata, A. K. P., & Vangoor, V. K. R. (2025). AIOPs in Action: Streamlining IT operations through Artificial intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5257975
[8] Abbas, S. I., & Garg, A. (2024). AIOps in DevOps: Leveraging Artificial Intelligence for Operations and Monitoring. AIOps in DevOps: Leveraging Artificial Intelligence for Operations and Monitoring, 64–70. https://doi.org/10.1109/icsadl61749.2024.00016
[9] Jain, N. S. (2023). Integrating Artificial Intelligence with DevOps: Enhancing continuous delivery, automation, and predictive analytics for high-performance software engineering. World Journal of Advanced Research and Reviews, 17(3), 1025–1043. https://doi.org/10.30574/wjarr.2023.17.3.0087
[10] Díaz-De-Arcaya, J., Torre-Bastida, A. I., Miñón, R., & Almeida, A. (2022). Orfeon: An AIOps framework for the goal-driven operationalization of distributed analytical pipelines. Future Generation Computer Systems, 140, 18–35. https://doi.org/10.1016/j.future.2022.10.008