AI-Powered Site Reliability Engineering: Integrating Intelligent Automation with Proven Design Patterns

Abstract

As the scale of modern service-based infrastructures grows beyond human ability to understand their functioning, the customary alerting frameworks (such as manually configured threshold alerts) become ineffective. Artificial intelligence and machine learning systems are being used in the reliability engineering field of cloud-native microservices to move from responding to problems after they happen to preventing and predicting them, especially because a failure can quickly impact other services. For instance, studies have demonstrated that neural network-based prediction systems can detect anomalous events before they affect a service's availability. This article discusses how SRE practices apply to AI-powered automation, as well as frameworks for anomaly detection algorithms, causal inference models and predictive analytics that enhance human decision-making. The article looks at machine learning models that help monitor infrastructure, tools for observing system performance, automated processes for handling incidents, smart ways to manage resource usage and updates, and systems that automatically spot changes in configuration while involving human oversight. Overall, the summary shows how smart systems find, examine, and connect the reasons for small drops in performance and make controlled fixes while following the principles of traditional reliability engineering.