构建人机协作新框架，让智能代理更可信、透明、易用。
Designing The Internet of Agents: A Framework for Trustworthy, Transparent, and Collaborative Human-Agent Interaction (HAX)
- 分三阶段设计，融合行为规则与结构化输出控制
- 提供可验证的混合主动性设计模式，支持意图预览与信任修复
- 适合开发下一代人机协同系统的研究者与工程师
生成式与自主代理的兴起标志着计算范式的根本转变，亟需重新思考人类如何与具有概率性、部分自主性的系统协作。本文提出人类-人工智能体验（HAX）框架，一种涵盖三个阶段的综合性方法，为可信、透明、协作的智能体交互建立设计基础。HAX整合了行为启发式、基于模式的SDK以确保结构化和安全输出，以及行为代理概念，用于协调代理活动以降低认知负担。一个经过验证的混合主动性设计模式目录，进一步支持意图预览、迭代对齐、信任修复及多代理叙事连贯性。该框架基于时间、互动与性能（TIP）理论，将多智能体系统重新定义为同事，首次实现了从信任理论、界面设计到基础设施的端到端贯通，为新兴的智能体互联网提供支持。
原文摘要 · Abstract (English)
The rise of generative and autonomous agents marks a fundamental shift in computing, demanding a rethinking of how humans collaborate with probabilistic, partially autonomous systems. We present the Human-AI-Experience (HAX) framework, a comprehensive, three-phase approach that establishes design foundations for trustworthy, transparent, and collaborative agentic interaction. HAX integrates behavioral heuristics, a schema-driven SDK enforcing structured and safe outputs, and a behavioral proxy concept that orchestrates agent activity to reduce cognitive load. A validated catalog of mixed-initiative design patterns further enables intent preview, iterative alignment, trust repair, and multi-agent narrative coherence. Grounded in Time, Interaction, and Performance (TIP) theory, HAX reframes multi-agent systems as colleagues, offering the first end-to-end framework that bridges trust theory, interface design, and infrastructure for the emerging Internet of Agents.
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