RoutingBench: Can Agentic Routing Analysis Scale to Production Datacenter Networks?

2026-09-19

AI Summary

研究通过探索更多、消化更少的原则策划的代理技能，解决了大规模网络路由路径分析的可扩展性问题。

Abstract

Recent advances in AI models and agentic technologies make AI for network operations (NetOps) within reach. However, scalability remains a key bottleneck of agentic NetOps when analyzing hyperscale networks, which comprise hundreds of datacenters, each housing thousands of network devices. The scalability challenge is rooted in the requirement of many NetOps tasks that must conduct global reasoning on how a local change of device behavior affects all relevant routing paths, known as routing-path analysis. This paper studies this scalability problem and evaluates how different agentic approaches, namely in-context learning, iterative reasoning, and agent skills, can scale routing-path analysis to large, complex networks. We present RoutingBench for evaluating agentic routing-path analysis, with varying network size and complexity, for various types of device changes. Our results show that agentic analysis is promising: agent skills curated with a principle termed "explore more; digest less" enables routing-path analysis on hyperscale networks of 50K routers with an accuracy of 99.5%, significantly outreaching the scalability of traditional symbolic analysis. Meanwhile, RoutingBench also reveals the boundary of AI agent capability on complex inter-datacenter networks and compound changes, posing open challenges for AI and agentic research.

Problem

• agentic NetOps
• routing-path analysis
• scalability
• hyperscale networks

Innovation

• agentic routing-path analysis
• scalability
• hyperscale networks
• agent skills
• in-context learning