Interaction Design for AI Agent Intelligence
Hyemin Lee · 2026
For years, personalization promised “accurate recommendations.” Now, people want to know why they got those results and how to change them. As AI agents become part of everyday life, users care less about perfect accuracy and more about trust. This study looks at how commercial agents like Alexa have evolved through different intelligence levels, showing how interactions should change at each stage. The key insight: go beyond simple “personalized delivery” to build transparency (what and why), predictability (consistent rules), and feedback (immediate and actionable) right into how products work. We analyzed real AI services—voice assistants, copilots, service bots—alongside public statistics from Korea’s online shopping trends. The result is a set of level-aligned interaction patterns and five design principles for trustworthy user experiences. Our findings show that organizations need evaluation systems tailored to their context, letting them check design quality before and after launch and steadily improve how they explain things and give users control.