A Review of Generative AI and DevOps Pipelines: CI/CD, Agentic Automation, MLOps Integration, and Large Language Models

This paper provides an overview of recent advancements and discussions surrounding Generative AI and its intersection with DevOps practices, leveraging a collection of contemporary research and articles. The increasing adoption of AI agents and machine learning models is transforming various aspects of software development and operations. This paper presents a comprehensive review of Generative AI applications in DevOps automation, covering 50 key research works published between 2023-2025. We analyze the transformative impact of AI-driven solutions across the software development lifecycle, including code generation, infrastructure management, continuous integration/delivery, and Kubernetes operations. The convergence of generative artificial intelligence (AI), intelligent agents, and automation is revolutionizing DevOps and cloud-native software engineering. This paper provides a comprehensive review of how generative AI and agentic workflows are transforming the development, deployment, and operation of modern software systems. We examine the integration of AI-driven automation in continuous integration and continuous deployment (CI/CD) pipelines, the adoption of cloud-native technologies such as Docker and Kubernetes, and the emergence of Infrastructure as Code (IaC) and progressive delivery models. The study highlights the benefits of these advancements, including increased efficiency, enhanced reliability, and accelerated innovation, while also addressing the challenges of security, compliance, observability, and skill development. By synthesizing insights from recent research and industry practice, this paper identifies the top terms, theories, and algorithms shaping the field and offers a forward-looking perspective on the evolution of AI-driven DevOps through 2029. The findings aim to guide researchers, practitioners, and organizations in leveraging the transformative potential of intelligent automation for resilient and adaptive software engineering. The review systematically examines how generative AI enhances deployment efficiency, monitoring capabilities, and overall development workflows while addressing challenges in cloud-native environments. Our analysis reveals emerging trends in AI agents for DevOps, containerized AI solutions, and the integration of large language models with existing DevOps toolchains.