Predictive Failure Recovery in Cloud-Native DevOps Pipelines Using Autonomous Multi-Agent AIOps Frameworks

Abstract

Cloud-native DevOps pipelines are becoming more complex, which makes them more likely to fail unexpectedly. These failures disrupt software delivery and lower system reliability. Traditional monitoring and incident-response methods depend a lot on manual work. This leads to longer recovery times and inconsistent quality in fixing issues. To tackle these problems, this research suggests an Autonomous Multi-Agent AIOps Framework for predicting failures and automating recovery in cloud-native DevOps settings. The framework includes specialized intelligent agents, like Data Collection Agents, Anomaly Detection Agents, Root-Cause Analysis Agents, and Auto-Remediation Agents, that work together across CI/CD workflows. By combining machine learning-based predictive analytics with event-driven automation, the system can predict failures before they happen and start self-healing actions with little input from humans. Tests on containerized microservices pipelines show significant improvements in Mean Time to Detect (MTTD), Mean Time to Recover (MTTR), and deployment downtime. The proposed framework improves reliability, scalability, and operational efficiency. This helps DevOps teams create truly autonomous, resilient, and adaptable cloud-native delivery pipelines.