Closed-Loop Feedback Systems in DevOps Automation Using AI for Predictive Failure Recovery and Deployment Rollbacks

Risa Minato Wataya

Closed-Loop Feedback Systems in DevOps Automation Using AI for Predictive Failure Recovery and Deployment Rollbacks

Authors

Risa Minato Wataya

AIOps Engineer / Intelligent DevOps Engineer , France

Keywords:

DevOps, Artificial Intelligence, Closed-loop Feedback, Deployment Rollbacks, Predictive Recovery, AIOps, CI/CD

Synopsis

The integration of Artificial Intelligence (AI) into DevOps pipelines has transformed modern software engineering by enabling closed-loop feedback systems that autonomously predict, detect, and recover from deployment failures. These intelligent systems significantly reduce Mean Time to Recovery (MTTR), enhance system availability, and automate rollback processes through proactive interventions. This paper explores the architecture, operational model, and advantages of AI-powered feedback mechanisms in continuous delivery environments. It also presents a critical analysis of existing approaches, outlines key components such as anomaly detection and automated rollback, and examines the measurable impact of AI in improving deployment resilience. Empirical metrics, conceptual diagrams, and comparative evaluations provide a comprehensive understanding of predictive intelligence in DevOps automation.

Author Biography

Risa Minato Wataya, AIOps Engineer / Intelligent DevOps Engineer , France

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