用确定性协议解决AI诊断网络时的乱码与安全问题
MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics
- 将命令行输出转为结构化JSON，确保AI准确理解
- 实现100%实体提取准确率，延迟增加不足0.9%
- 强制人工确认机制，适合对安全要求高的运维场景
大型语言模型（LLMs）在智能网络运维（AIOps）中的应用面临两大挑战：一是随机性根基问题，即LLM难以可靠解析厂商特定的非结构化命令行输出；二是赋予自主代理终端访问权限带来的安全缺口。本文提出MCP-Diag，一种基于模型上下文协议（MCP）的混合神经符号架构。设计确定性转换层，将标准工具（dig、ping、traceroute）的原始输出转化为严格遵循JSON模式的数据，再交由AI处理。进一步引入强制“引出循环”机制，在协议层强制执行人机协同授权（HITL）。初步评估显示，MCP-Diag实现100%实体提取准确率，执行延迟增加低于0.9%，上下文令牌使用量提升3.7倍。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) into network operations (AIOps) is hindered by two fundamental challenges: the stochastic grounding problem, where LLMs struggle to reliably parse unstructured, vendor-specific CLI output, and the security gap of granting autonomous agents shell access. This paper introduces MCP-Diag, a hybrid neuro-symbolic architecture built upon the Model Context Protocol (MCP). We propose a deterministic translation layer that converts raw stdout from canonical utilities (dig, ping, traceroute) into rigorous JSON schemas before AI ingestion. We further introduce a mandatory "Elicitation Loop" that enforces Human-in-the-Loop (HITL) authorization at the protocol level. Our preliminary evaluation demonstrates that MCP-Diag achieving 100% entity extraction accuracy with less than 0.9% execution latency overhead and 3.7x increase in context token usage.
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