Designing The Internet of Agents: A Framework for Trustworthy, Transparent, and Collaborative Human-Agent Interaction (HAX)

TL;DR

The paper introduces the HAX framework, a comprehensive approach for designing trustworthy, transparent, and collaborative human-agent interactions in the emerging Internet of Agents, integrating behavioral, structural, and orchestration components.

Contribution

It presents the first end-to-end framework that combines trust theory, interface design, and infrastructure for multi-agent systems as colleagues.

Findings

Validated catalog of mixed-initiative design patterns

Framework reduces cognitive load in human-agent interaction

Reframes multi-agent systems as collaborative colleagues

Abstract

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.