LLM Framework for Enhanced CI/CD Pipelines for Intelligent DevOps

Abstract

This paper introduces an AI-augmented CI/CD framework that integrates Large Language Models into traditional DevOps pipelines, 
addressing common operational challenges including build failures, alert fatigue, and deployment bottlenecks. Our approach 
reimagines LLMs as intelligent DevOps assistants rather than merely deployment targets. Through systematic evaluation across 
multiple enterprises, we demonstrate that our framework reduces Mean Time to Recovery (MTTR) by 76%, decreases alert volume 
by 75%, and increases developer productivity by 35% with minimal operational overhead. We provide detailed mappings between 
DevOps processes and specialized LLM capabilities, along with implementation patterns that can be integrated into existing CI/
 CD tools. Our work establishes a new paradigm for embedding AI assistance directly into development workflows, creating more 
resilient systems while improving the overall developer experience.