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@article{182367,
        author = {Devashish Ghanshyambhai Patel},
        title = {Generative AI in DevOps: Enhancing Cloud Workflow Automation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3732-3742},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182367},
        abstract = {The fusion of Generative AI and DevOps is reshaping the landscape of cloud computing by introducing dynamic intelligence into automated software delivery pipelines. Traditionally, DevOps has relied on deterministic scripts and manual configurations to manage infrastructure, CI/CD workflows, and system operations. However, as applications scale across hybrid and multi-cloud environments, these static approaches face limitations in flexibility, responsiveness, and resilience. Generative AI addresses these challenges by leveraging large language models (LLMs) and agentic architectures to understand context, generate code, interpret telemetry, and take proactive actions.
This paper explores how generative AI models are revolutionizing cloud workflow automation within the DevOps lifecycle. It provides a comprehensive view of key capabilities such as real-time infrastructure generation, intelligent pipeline restructuring, root cause analysis, automated remediation, policy-as-code enforcement, and synthetic documentation. Through a blend of technical exposition, architectural diagrams, tool reviews, and case-driven analysis, we highlight how generative AI augments human engineers, reduces operational friction, and accelerates time-to-value.
We further analyze the evolving ecosystem of platforms such as GPT-4 Turbo or GPT-4o, LangChain, GitHub Copilot, and AutoGPT, which enable seamless integration of AI into DevOps workflows. In doing so, we also address current challenges—including model hallucination, security vulnerabilities, integration overhead, and governance concerns—and propose strategies to mitigate them.
Our research demonstrates that generative AI is not just a tool for automation but a catalyst for building self-optimizing, context-aware, and resilient DevOps systems. As organizations adopt these technologies, they will transition from reactive incident handling to predictive and autonomous operations, setting the stage for the next era of intelligent cloud engineering.},
        keywords = {Generative AI, DevOps Automation, Cloud Workflow Optimization, Large Language Models (LLMs)},
        month = {July},
        }