A Closed-Loop Agentic AI Framework for Self -Configuring, Self-Healing, and Optimized CI/CD Automation

Abstract

Static continuous integration and continuous delivery pipelines remain difficult to adapt when repositories, dependencies, deployment targets, and operational conditions change. Manual configuration also slows failure diagnosis and prevents pipelines from learning from previous executions. This paper presents DevOps-Pilot, a closed-loop agentic AI framework for intelligent DevOps automation. The framework analyses repository context, detects languages, dependency files, build tools, test frameworks, Docker assets, and Kubernetes manifests, and then uses LLM-assisted planning to generate Jenkinscompatible pipeline definitions. Pipeline execution is monitored through a Flask dashboard and SQLite-backed history store, while anomaly detection and self-healing logic recommend recovery actions for installation, testing, and deployment failures. A reinforcement learning-inspired optimisation policy updates cache, parallel testing, retry, and timeout settings from observed rewards. The academic prototype was evaluated in simulation mode across four repositories and nine pipeline runs. Results show six successful and three failed runs, a success rate of 66.67%, an average execution time of 120.52 seconds, and an average reward of 3.2641. The findings indicate the feasibility of closed-loop CI/CD automation for adaptive software delivery.