AI-Augmented DevOps: Autonomous Software Delivery with Large Language Models

The evolution of DevOps has accelerated software delivery through continuous integration, deployment, and infrastructure automation. However, modern systems' scale, dynamism, and complexity have outpaced traditional automation techniques. This paper introduces AI-augmented DevOps: an architectural and operational model that embeds intelligent agents powered by Large Language Models (LLMs) and machine learning into the DevOps lifecycle. We propose a modular five-layer framework consisting of observation, inference, action, feedback, and interaction layers, each designed to support autonomous, traceable, and policy-compliant decision-making. Our implementation leverages GPT-4 and reinforcement learning to enhance tasks such as log summarization, Infrastructure-As-Code (IaC) generation, and real-time incident remediation. A simulated CI/CD environment and real-world case studies demonstrate significant improvements in deployment frequency, MTTR, and change failure rates. The paper provides a blueprint for integrating AI into software delivery pipelines, enabling systems that continuously learn, adapt, and improve while maintaining human oversight and operational governance.