Autonomous Remediation and Agentic Sre Teams: Reinforcement Learning For Self-Healing Infrastructure and Human-Agent Collaboration in Incident Response

Abstract

Modern distributed systems generate thousands of alerts daily, overwhelming Site Reliability Engineering (SRE) teams and creating response bottlenecks that extend Mean Time to Resolution (MTTR) and impact service availability. This research develops an integrated framework combining reinforcement learning-based autonomous remediation with human-agent collaboration models to create self-healing infrastructure capabilities while maintaining appropriate human oversight. Through implementation across five organizations operating large-scale distributed systems, we demonstrate that RL agents trained on historical incident data achieve 73% accuracy in root cause identification and successfully execute automated remediation for 64% of common failure scenarios without human intervention. For complex incidents requiring human judgment, our agentic SRE team model reduces MTTR by 58% through intelligent agent assistance including automated investigation, evidence gathering, and remediation recommendation. The framework employs deep Q-networks that learn optimal diagnostic and remediation policies from 2.4 million historical incidents, combined with collaborative agent architectures where specialized agents handle distinct operational domains while coordinating through shared state representations. Validation demonstrates 67% reduction in overall incident response time, 81% decrease in alert fatigue through intelligent triage, and maintained 99.95% availability despite 34% reduction in on-call SRE hours. However, the research also identifies critical limitations including RL agent performance degradation on novel failure modes, trust calibration challenges affecting human-agent handoffs, and governance complexities around autonomous system actions. These findings contribute both theoretical advances in applying reinforcement learning to operational problem spaces and practical frameworks enabling organizations to augment SRE capabilities through autonomous agents while preserving human judgment for ambiguous situations.