提出ANX协议与3EX架构，让AI Agent交互更高效安全
ANX: Protocol-First Design for AI Agent Interaction with a Supporting 3EX Decoupled Architecture
- 设计Agent原生协议，用标记语言和命令行减少冗余信息
- 实验显示比MCP少用55.6%令牌，执行时间缩短57.7%
- 适合需要高安全性与长程协作的AI系统开发者
AI代理作为自主数字实体，亟需原生协议支持。现有方法如GUI自动化和基于MCP的技能存在高令牌消耗、交互碎片化、安全性不足等问题，因缺乏统一顶层框架与核心组件，各模块独立且有缺陷。为此，我们提出ANX——一个开放、可扩展、可验证的原生协议及顶层框架，整合命令行、技能、MCP，通过协议创新、架构优化与工具补充解决痛点。其四大核心创新：1）原生设计（ANX Config、Markup、CLI），信息密度高、灵活适应性强，降低令牌使用并消除不一致；2）人机交互结合技能灵活性，支持双渲染：既可执行指令，又可生成人类可读界面；3）基于MCP的按需轻量应用，无需预注册；4）ANX Markup支持机器可执行标准操作流程，消除歧义，保障长周期任务与多代理协作可靠性。作为系列首篇，重点阐述设计，提出3EX解耦架构与ANXHub，并完成可行性分析与实验验证。ANX实现原生安全：通过绕过LLM的UI-to-Core通信，敏感数据不进入代理上下文；仅由人类确认防止自动化滥用。在表单填写实验中，相比MCP技能，Qwen3.5-plus与GPT-4o分别减少47.3%和55.6%令牌；相比GUI自动化，分别减少57.1%和66.3%；执行时间缩短58.1%和57.7%。
原文摘要 · Abstract (English)
AI agents, autonomous digital actors, need agent-native protocols; existing methods include GUI automation and MCP-based skills, with defects of high token consumption, fragmented interaction, inadequate security, due to lacking a unified top-level framework and key components, each independent module flawed. To address these issues, we present ANX, an open, extensible, verifiable agent-native protocol and top-level framework integrating CLI, Skill, MCP, resolving pain points via protocol innovation, architectural optimization and tool supplementation. Its four core innovations: 1) Agent-native design (ANX Config, Markup, CLI) with high information density, flexibility and strong adaptability to reduce tokens and eliminate inconsistencies; 2) Human-agent interaction combining Skill's flexibility for dual rendering as agent-executable instructions and human-readable UI; 3) MCP-supported on-demand lightweight apps without pre-registration; 4) ANX Markup-enabled machine-executable SOPs eliminating ambiguity for reliable long-horizon tasks and multi-agent collaboration. As the first in a series, we focus on ANX's design, present its 3EX decoupled architecture with ANXHub and preliminary feasibility analysis and experimental validation. ANX ensures native security: LLM-bypassed UI-to-Core communication keeps sensitive data out of agent context; human-only confirmation prevents automated misuse. Form-filling experiments with Qwen3.5-plus/GPT-4o show ANX reduces tokens by 47.3% (Qwen3.5-plus) and 55.6% (GPT-4o) vs MCP-based skills, 57.1% (Qwen3.5-plus) and 66.3% (GPT-4o) vs GUI automation, and shortens execution time by 58.1% and 57.7% vs MCP-based skills.
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