arXiv:2608.14574v1 Announce Type: cross
Abstract: The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-driven troubleshooting has evolved through scripted automation, rule-based systems, and AI-assisted operations into fully autonomous incident resolution. This paper traces the evolution of AI operations (AIOps) in cloud network infrastructure, identifying the architectural patterns, organizational challenges, and technical inflection points that enabled each generational transition. Drawing from production experience operating network infrastructure at hyperscale, we present a maturity model that characterizes five distinct operational generations, analyze the technical and organizational barriers that impede transitions between generations, and document the metrics that indicate readiness for increased autonomy. We show that the path from reactive to autonomous operations is not merely a technology problem but requires co-evolution of tooling, trust frameworks, knowledge management practices, and operational culture. Our findings provide a practical roadmap for infrastructure organizations seeking to adopt progressively autonomous AI operations.
Abstract: The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-driven troubleshooting has evolved through scripted automation, rule-based systems, and AI-assisted operations into fully autonomous incident resolution. This paper traces the evolution of AI operations (AIOps) in cloud network infrastructure, identifying the architectural patterns, organizational challenges, and technical inflection points that enabled each generational transition. Drawing from production experience operating network infrastructure at hyperscale, we present a maturity model that characterizes five distinct operational generations, analyze the technical and organizational barriers that impede transitions between generations, and document the metrics that indicate readiness for increased autonomy. We show that the path from reactive to autonomous operations is not merely a technology problem but requires co-evolution of tooling, trust frameworks, knowledge management practices, and operational culture. Our findings provide a practical roadmap for infrastructure organizations seeking to adopt progressively autonomous AI operations.
This article has been indexed from cs.AI updates on arXiv.org
Read the original article: