Auto-Devops GPT: An Agentic AI Framework for Self-Healing CI/CD Pipelines Using LLM-Based Root Cause Analysis and Reinforcement Learning

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• Semantic Scholar2026-07-16 23:31:51
  Auto-Devops GPT: An Agentic AI Framework for Self-Healing CI/CD Pipelines Using LLM-Based Root Cause Analysis and Reinforcement Learning

  Continuous Integration and Continuous Deployment (CI/CD) pipelines are essential for modern software delivery, but frequent failures in build, test, dependency, configuration and deployment stages reduce reliability and increase recovery time. Traditional CI/CD tools execute predefined workflows but generally require manual log inspection and human intervention after failures. This paper proposes AutoDevOps GPT, an agentic artificial intelligence framework for self-healing CI/CD pipelines. The framework integrates Jenkins webhook events, log-based failure classification, Large Language Model-assisted root cause analysis, multi-agent recovery planning, safe remediation execution, incident storage and reinforcement learning-based recovery optimisation. The proposed system uses an Analyzer Agent to classify failures, a Planner Agent to select recovery actions and an Executor Agent to apply predefined safe remediation. A lightweight Q-learning-inspired policy updates recovery action preferences using success, time and cost feedback. The system is implemented using Python Flask, SQLite, SQLAlchemy, Jenkins, GitHub, Docker, Kubernetes support and a dashboard for incident intelligence. Experimental evaluation shows improvementsin detection time, recovery time, recovery accuracy and automation efficiency compared with traditional CI/CD, rulebased recovery and basic AIOps baselines.