🤖 AI Summary
This work addresses the challenges of frequent, rapidly propagating, and complex failures in hyperscale cloud networks, which are difficult to manage efficiently through traditional manual operations. The authors propose a multi-agent collaborative architecture with progressive autonomy, featuring hierarchical agent design, skill-driven standardized tool invocation, structured encoding of operational knowledge, layered security controls, and closed-loop validation mechanisms to enable automated fault detection, diagnosis, and remediation. Deployed in production environments of major cloud providers, the system achieves over 90% autonomous resolution rates for common failure categories, significantly enhancing operational autonomy while ensuring safety and reliability.
📝 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.