
Traditional A/B testing splits traffic evenly, but multi-armed bandits dynamically send more users to the better-performing version. Here’s how the method works, where it helps, and when UX teams should use it over classic A/B testing.

AI agent simulations promise faster, lower-risk UX testing by replacing real users with AI-simulated personas. Here’s how the method works, where it falls short, and when designers should rely on simulated users versus real user testing.

A/B testing is great for comparing two versions of a design, but multivariate testing helps teams evaluate multiple design element combinations at once. Here’s how both methods work, how they differ, and when UX teams should use each one.

Design engineering has always lived between design and code. But with AI tools turning prompts into interfaces and code into editable canvases, that bridge is becoming a new way of building.