Autonomous SRE: A Reinforcement Learning Approach to Proactive Incident Prevention in Cloud-Native Environments

Abstract

The autonomous SRE agent represents a significant advancement in cloud-native reliability engineering by implementing reinforcement learning and large language models to create self-healing systems. This innovation addresses critical challenges in modern distributed architectures where traditional human-centered operations struggle with increasing complexity and deployment velocity. By continuously monitoring telemetry data, constructing sophisticated state representations, and implementing preventive measures without human intervention, the autonomous agent transforms operational practices from reactive firefighting to proactive reliability management. The architecture integrates with various cloud platforms through specialized adapters and implements distributed systems design patterns to ensure resilience. Experimental evaluation across diverse environments demonstrates substantial improvements in incident reduction, response time, and operational efficiency compared to conventional monitoring systems. While certain constraints exist, particularly for novel failure modes and rapidly propagating issues, the agent's ability to learn continuously from experience points toward a future of increasingly autonomous cloud infrastructure management that allows engineering teams to focus on strategic improvements rather than repetitive maintenance tasks, ultimately delivering enhanced system reliability and reduced operational burden.