Ai-Driven Devops Automation for Ci/Cd Pipeline Optimization
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Abstract
Modern CI/CD pipelines have become cognitively overloaded, policy-fragile, and operationally inefficient as software delivery scales across microservices, multi-cloud platforms, and regulated environments. While DevOps automation has improved deployment velocity, it remains largely rule-based, reactive, and incapable of reasoning over complex pipeline behavior, failure patterns, or governance constraints. This paper introduces a systemic, AI-driven DevOps automation framework designed to optimize CI/CD pipelines through continuous learning, risk-aware decision-making, and policy-aligned control. The core contribution is a closed-loop, intelligence-driven control plane that integrates telemetry inference, pipeline behavior modeling, and constrained decision automation to optimize build reliability, deployment throughput, and operational toil while preserving human oversight and enterprise governance. Unlike existing approaches that focus on isolated optimizations or tool-level enhancements, the proposed framework treats CI/CD as a distributed socio-technical system, addressing failure modes related to scale, drift, cognitive load, and compliance. We describe the architecture, lifecycle control flow, and governance mechanisms of the proposed system, and evaluate its impact using operational metrics such as mean time to detection (MTTD), mean time to recovery (MTTR), pipeline failure recurrence, and policy deviation rates. The results demonstrate that AI-driven DevOps automation, when designed as a governed control system rather than an autonomous executor, can materially improve reliability, safety, and delivery efficiency in enterprise CI/CD environments.
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