Autonomous Incident Remediation via GenAI-Assisted Runbooks

Abstract

Cloud infrastructure has evolved to form the backbone of global digital operations; however, reliability engineering has not kept pace with this growing complexity. While observability and alerting systems have matured considerably, incident response still relies heavily on human expertise, thus introducing a significant gap between detection and automated remediation that translates into very costly downtime and operational fatigue. This article introduces a closed-loop remediation framework powered by Generative AI, enabling the diagnosis, execution, and validation of incident resolution with appropriate safety guardrails and auditability. Within this framework, the model integrates LLM-based diagnostics, policy-driven execution, and safety validation mechanisms, with continuous learning feedback cycles. The article draws best practices from multi-cloud implementation and puts forward a phased implementation approach while discussing governance considerations toward autonomous remediation. This framework demonstrates substantial improvement in incident resolution speed and reduction in manual escalations, thus positioning autonomous incident remediation as the cornerstone for the next evolution in AI-powered reliability engineering. As systems continue to scale beyond human cognitive limits, such autonomous approaches are not just advantageous but also become very key for operational resilience amidst ever-increasing digital environment complexities.