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.

翻译：AI智能体作为自主数字行动者，需要智能体原生协议。现有方法包括GUI自动化和基于MCP的技能，但由于缺乏统一顶层框架和关键组件，各独立模块存在缺陷，导致令牌消耗高、交互碎片化、安全性不足。为应对这些问题，我们提出ANX——一种开放、可扩展、可验证的智能体原生协议与顶层框架，融合CLI、技能、MCP，通过协议创新、架构优化和工具补充解决痛点。其四大核心创新：1) 智能体原生设计（ANX配置、标记、CLI）具有高信息密度、灵活性和强适应性，可减少令牌并消除不一致性；2) 人机交互结合技能灵活性实现双重渲染——即可供智能体执行的指令和人类可读的UI；3) 基于MCP的按需轻量级应用无需预注册；4) 支持ANX标记的机器可执行SOP消除歧义，实现可靠的长时任务和多智能体协作。作为系列首篇，我们聚焦ANX设计，呈现其3EX解耦架构与ANXHub，并进行初步可行性分析和实验验证。ANX确保原生安全性：经由LLM绕过的UI到核心通信机制使敏感数据远离智能体上下文；仅人类确认机制防止自动滥用。基于Qwen3.5-plus/GPT-4o的表单填写实验显示：相比基于MCP的技能，ANX减少令牌消耗47.3%（Qwen3.5-plus）和55.6%（GPT-4o）；相比GUI自动化，减少57.1%（Qwen3.5-plus）和66.3%（GPT-4o）；执行时间较基于MCP的技能缩短58.1%和57.7%。