Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
2026-06-08 • Software Engineering
Software EngineeringArtificial IntelligenceEmerging TechnologiesMultiagent SystemsNetworking and Internet Architecture
AI summaryⓘ
The authors created a system where multiple AI agents work together to find and fix problems in huge cloud networks without humans needing to step in. These AI agents follow clear rules and use knowledge from operational guides to handle incidents safely and effectively. The system has been tested in a real cloud environment, fixing over 90% of common issues automatically while ensuring problems can be undone if needed. The authors also share challenges and lessons learned from running these AI agents at a large scale.
Agentic AIMulti-agent SystemsNetwork Incident ManagementAutonomous RemediationHierarchical DecompositionOperational RunbooksSafety BoundariesClosed-loop VerificationHyperscale Cloud InfrastructureRollback Mechanisms
Authors
Arun Malik
Abstract
Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures. This paper presents an agentic AI architecture for autonomous incident resolution in large-scale network operations. Our system employs a multi-agent orchestration framework where specialized AI agents collaborate to detect, diagnose, and remediate network incidents without human intervention. We describe the architectural principles, including hierarchical agent decomposition, skills-based tool invocation via standardized protocols, structured knowledge encoding from operational runbooks, progressive autonomy with safety boundaries, and closed-loop verification. The architecture has been deployed in production at a major cloud provider, demonstrating that agentic AI systems can achieve autonomous resolution rates exceeding 90% for common incident categories while maintaining safety guarantees through layered authorization and rollback mechanisms. We discuss design tradeoffs, failure modes, and lessons learned from operating autonomous AI agents at scale.