Skip to content
Review

A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace

Jul 2026 · arXiv.org · Vol abs/2607.19941 · 0 citations · 8 references
Computer Science

TL;DR

This 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.

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.

View source

Similar papers

Toward User-Orchestrated Human–Multi-Agent Teams for Co-Creation

It is argued that creative co-creation calls instead for user-orchestrated Human–Multi-Agent Teams (HMATs), teams in which humans participate alongside multiple agents while retaining the ability to keep the team aligned with the evolving purposes of the work.

Hyunseung Lim, Hwajung Hong · 0 citations
#artificial intelligence Review Sep 2026

When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration

This work establishes a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose, and outlines how organisations, researchers, and government lead...

Civic-Ai Collaboration Jiaying Wu, Caleb Ziems, Raymond Chan et al. · 0 citations
Review Open access Sep 2026

Trust and interaction design in AI-enabled systems: a systematic literature review

This systematic literature review analyzes 1,565 peer-reviewed scholarly articles published post-2023 and identifies nine key design factors, including explainability, anthropomorphism, confidence, interface, errors, security, transparency, competence, and perceived control.

Summer L. Abramson, Jhonathan Sora-Cardenas, Pranav Nandakumar et al. · 0 citations
Book Open access Sep 2026

Identifying Interaction Dimensions and their Relation to Agent Behavior in Human-AI Teams

This work provides a new framework for AI agent designers to understand user behavior and guidelines for better supporting it, and observed that while users self-report goal prioritization independently of agent behavior, in-game logs reveal that non-collaborative goals are prioritized significantly more often with AI...

Inês Lobo, Janin Koch, Jennifer Renoux et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.