Designing The Internet of Agents: A Framework for Trustworthy, Transparent, and Collaborative Human-Agent Interaction (HAX)
This work addresses the problem of human-agent collaboration for developers and users in the emerging Internet of Agents, presenting a foundational but incremental framework that bridges trust theory, interface design, and infrastructure.
The paper tackles the challenge of designing trustworthy and collaborative interactions between humans and autonomous agents by introducing the Human-AI-Experience (HAX) framework, which integrates behavioral heuristics, a schema-driven SDK, and design patterns to reduce cognitive load and enhance multi-agent coherence.
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.