用智能代理系统自动处理大规模网络故障，解决人工响应慢的问题。
Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
- 多智能体协作架构，分工检测、诊断、修复网络故障
- 90%以上常见故障可自主解决，且有安全回滚机制保障
- 适合超大规模云运维团队，提升故障响应效率
超大规模云网络基础设施面临故障数量多、速度快、复杂度高的运营挑战，传统人工响应已难以应对。本文提出一种用于大规模网络运维的智能体式人工智能架构，通过多智能体协同，实现故障的自主检测、诊断与修复。系统采用分层智能体分解、基于技能的标准化工具调用、运行手册结构化知识编码、渐进式自治与安全边界控制、闭环验证等设计原则。该架构已在某大型云服务商生产环境部署，实测表明对常见故障类别，自主解决率超过90%，并通过分层授权与回滚机制确保安全性。文中还探讨了设计权衡、失效模式及规模化运行的经验教训。
原文摘要 · Abstract (English)
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.
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