Self-Healing CI/CD Pipelines Using AI Agents: An Autonomous Failure Detection and Recovery

Continuous Integration and Continuous Deployment (CI/CD) pipelines represent the operational backbone of modern software delivery. Despite their critical role, these pipelines remain susceptible to a broad class of runtime failures — ranging from unresolved dependency conflicts to misconfigured environment variables — that demand significant manual intervention and introduce measurable delivery latency. This paper presents SHIELD (Self-Healing Intelligent Engine for Log-Driven pipelines), a novel AI-agent-based framework designed to autonomously detect, diagnose, and remediate pipeline failures without human involvement. SHIELD integrates a multi-layer monitoring architecture with a hybrid reasoning engine that combines deterministic pattern matching with large language model (LLM)-driven root cause inference. Empirical evaluation across five controlled failure scenarios demonstrates a mean time-to-recovery (MTTR) reduction of 74.3% and a first-attempt autonomous fix success rate of 82.6% compared to manual debugging baselines. The framework is implemented atop GitHub Actions and Jenkins with Docker-based execution environments, and is designed as a plug-and-play module requiring no modifications to existing pipeline definitions. These results suggest that AI-augmented self-healing is a tractable and practically deployable strategy for improving DevOps reliability at scale.