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This study establishes eight core UX principles designed to guide the design of human-AI agent interactions within enterprise workflows, prioritizing trust and successful adoption over raw model capabilities. By synthes participatory design workshops with expert reviews, it offers actionable guardrails for engineers developing agentic AI systems that remain centered on user needs rather than incidental software features.
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A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace

arXiv2 min read

arXiv:2607.19941v1 Announce Type: cross Abstract: As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust and successful adoption. To address this, our study uses a multi-method approach - combining participatory design workshop, paper-and-pencil, expert review, meta-analysis, and in-depth interviews - to identify and validate a design framework of eight core UX principles for human-AI agent interaction in the workplace. Together with their underlying criteria, these principles provide actionable guardrails for designers and software engineers, creating a foundation for developing effective and human-centered AI agent interactions. This study contributes to a structured foundation for future empirical studies on agentic AI in enterprise settings.

Abstract:As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust and successful adoption. To address this, our study uses a multi-method approach - combining participatory design workshop, paper-and-pencil, expert review, meta-analysis, and in-depth interviews - to identify and validate a design framework of eight core UX principles for human-AI agent interaction in the workplace. Together with their underlying criteria, these principles provide actionable guardrails for designers and software engineers, creating a foundation for developing effective and human-centered AI agent interactions. This study contributes to a structured foundation for future empirical studies on agentic AI in enterprise settings.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2607.19941 [cs.HC]

(or arXiv:2607.19941v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2607.19941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 204 words

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