用智能体架构提升网络配置修复的准确率和安全性
Evaluating Agentic Configuration Repair for Computer Networks
- 构建智能体系统，动态管理上下文并迭代验证修复方案
- 相比基础大模型，修复成功率平均提升12%，安全率提升17%
- 适合网络运维、自动化工具研发人员参考
计算机网络中的配置错误仍是导致互联网重大中断的主要原因。研究开始借助大语言模型（LLMs）自动化这一复杂且易出错的网络配置任务。然而，即使是当前最先进的模型，在大规模复杂场景下仍无法有效修复配置错误，且常引入新问题。本文对开源与闭源的LLMs进行评估，其均结合了形式化网络验证和上下文检索工具。结果表明，采用智能体架构的系统在修复有效性上平均提升12%，在安全性上平均提升17%。这得益于其动态管理上下文与迭代验证修复的能力。
原文摘要 · Abstract (English)
Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automate the complex, error-prone task of network configuration. However, even state-of-the-art models fail to resolve misconfigurations in large-scale, complex scenarios and often introduce new errors. In this work, we benchmark open- and closed-source LLMs augmented with formal network verification and context retrieval tools. We demonstrate that agentic architectures outperform base LLMs in repair efficacy (by 12% on average) and safety (by 17% on average), enabled by the ability to dynamically manage context and iteratively validate configuration repairs.
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