What is Generative UI, and how does it differ from using an AI design tool to build an interface?
Generative UI means the interface itself is produced or adjusted by AI at runtime: depending on what the user is doing, the AI decides which components to show, how to arrange them and which interaction patterns to use. One common definition is that parts of the user interface are generated, selected or controlled by an AI Agent at runtime.
The difference from tools such as v0, Lovable or Figma AI is timing. Those help designers or developers produce a fixed interface during development, then ship it. Generative UI operates at use time, where each interaction may yield a different screen.
Why did Generative UI emerge, and what problem is it trying to solve?
Traditional interfaces are fixed layouts designed for an "average user," but user goals change every time: one person wants a quick number, another wants to compare three options. As AI assistants spread, a natural question arose: if AI can understand intent in the moment, should the interface adapt too, instead of always returning a Block of text or a fixed form?
Google's A2UI spec, released in late 2025, lets agents produce inline widgets such as forms and sliders, and Gemini's Dynamic View generates interactive interfaces from prompts. Both are concrete examples of the same shift: part of the decision about what to show is handed to AI.
How does Generative UI actually work?
CopilotKit's breakdown describes three patterns that differ in how much freedom the AI gets. In the static pattern the app owns the UI and the agent only picks a predefined component and fills it with data. In the declarative pattern the agent returns a structured description of the interface (cards, lists, forms) that the frontend renders under its own styling rules. In the open-ended pattern the agent supplies a whole UI surface that the frontend mostly just hosts.
Control decreases and freedom increases from the first to the last. Related technologies include Google's A2UI (a declarative spec) and AG-UI (a bidirectional event protocol between agent and frontend), which operate at different layers rather than replacing each other.
What's the practical impact for designers and product teams?
The biggest shift is that the object of design moves from one fixed screen to a set of rules: which components AI may choose, which regions may change and which must stay put. Risks listed in Eleken's write-up include broken muscle memory (an interface that looks different every time forces relearning), the same prompt not yielding the same interface, hallucinated components behaving wrongly, and inconsistent output when there is no clean, constrained Design System.
The compromise it favors is a hybrid UI: stable global navigation and structure, with only specific regions adapting, and familiar, predictable interfaces kept for high-risk tasks.
Google's A2UI is a declarative spec that lets agents produce inline widgets such as forms and sliders within a conversation, rendered by the frontend under its own styling rules. Notion AI and Salesforce Einstein are also commonly cited as related examples: the former adapts the editing environment to the task, the latter adapts dashboard layouts to role and usage (these come from Eleken's July 2026 update, not from the companies' own documentation).
The upside is an interface that fits the user's goal in the moment, without building a fixed screen for every situation; the downside is lower predictability, inconsistent results for the same prompt, hallucinations that can cause wrong behavior, security and performance concerns for open-ended approaches, and heavy dependence on a clean, constrained design system.