打造开放平台，让普通人也能轻松测试和对比AI网络诊断工具。
Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting
- 设计一个标准化平台，降低AI网络故障诊断工具的开发门槛。
- 支持可复现的实验与评估，提升AI Agent在实际场景中的可信度。
- 适合研究人员和工程师快速验证新想法，推动AI网络运维普及。
近期研究已证明人工智能，特别是大语言模型（LLMs），在支持网络配置生成、自动化网络诊断等任务中具有有效性。本文作为初步工作，聚焦于将AI Agent应用于网络故障排查，强调需要一个标准化、可复现且开放的基准测试平台，以便以较低操作成本构建和评估AI Agent。
原文摘要 · Abstract (English)
Recent research has demonstrated the effectiveness of Artificial Intelligence (AI), and more specifically, Large Language Models (LLMs), in supporting network configuration synthesis and automating network diagnosis tasks, among others. In this preliminary work, we restrict our focus to the application of AI agents to network troubleshooting and elaborate on the need for a standardized, reproducible, and open benchmarking platform, where to build and evaluate AI agents with low operational effort.
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