What Is Generative UI? The Interface That Builds Itself Around You

Traditional software relies on pre-authored, static screens. A team of designers and engineers decides, ahead of time, exactly what every screen looks like, and every user sees the same interface regardless of what they actually need. That works fine when everyone’s task is roughly the same. It works less well the moment your situation is even a little different from the average user’s, which, if you think about it, is most of the time.

Generative UI flips that around. Instead of one screen trying to fit everyone, the AI can shape dynamic interfaces around a specific question and a specific set of data, in the moment someone needs them. Google’s own research team put it simply: instead of returning a wall of text, an AI can return something visual and interactive, built for that exact request. Their internal testing found that people strongly preferred that experience over a plain-text answer.

That’s the promise. It’s also, if handled carelessly, the risk.

Not every version of generative UI hands the AI the same amount of freedom, and that difference matters more than most people realize. There are different types of UI generation, but most implementations break down into three approaches: static, declarative, and open-ended.

Static generative UI is component selection, not creation. A developer builds a fixed set of UI components ahead of time (a weather card, a comparison table, a pricing widget), and a catalog describes that predefined set. Static generative UI uses these predefined components for rendering, so the AI’s only job is to pick the right one and fill it in with the right data or input. This is the highest-control approach, offering more predictability but limited visual flexibility: nothing renders that a person didn’t already design and test.

Declarative generative UI sits in the middle. Instead of choosing from a small, fixed library, the agent returns a structured description of what it wants (this many cards, this data, in what order), and the frontend builds the actual screen using its own components and its own style. Google’s protocol for this, called A2UI, works exactly this way: the AI sends the description as a steady, secure stream of small JSON messages, a format known as JSONL, never actual code, and the client app defines how to render it. Because the AI hands over structured output instead of executable code, there’s no raw code running that a person didn’t write, which closes off a lot of security risk.

Open-ended generative UI is the least constrained. Here, the AI can generate an entire interface itself: real HTML, real JS, sometimes something closer to a small web page. It’s the most flexible option and also the riskiest, since nothing stops the result from looking off-brand, being hard to use, or opening a genuine security hole.

Whichever of the three approaches a team picks, that output still has to travel from the AI agent to the app in real time, and that’s a separate problem from deciding what the interface looks like.

That’s what AG-UI, short for Agent-User Interaction, solves. It’s an open protocol built to keep an AI agent and a user-facing app talking to each other as a task unfolds: agents emit updates as they work, and the client listens for that response and updates the interface in turn, instead of freezing the screen until the whole answer is ready. For example, the same event stream can drive a chat widget or a full dashboard without the frontend team rewriting anything. AG-UI is framework agnostic, meaning it works the same way whether the frontend is built in React, Angular, or something else, and it sits low in the stack, underneath all three approaches above, regardless of which one a team picks. AG-UI is also designed to integrate with other standards, including A2UI, MCP-UI, and Open-JSON-UI, acting as connective tissue no matter which specification sits on top of it.

Structured output is worth understanding on its own, since it’s the piece that makes most of this safe to ship.

Whether it’s A2UI’s JSON schema or another format, the idea is the same: instead of asking an AI to write code, a system asks it to fill out a form. The AI’s response gets checked against that schema in a validation step before anything renders on screen, which is what catches a broken or malformed answer before it ever reaches a real user. That single choice, data instead of code, is why teams can trust an AI-generated response the same way they’d trust output from a system they built and tested themselves.

A newer standard called MCP Apps takes a related but different approach. Instead of describing an interface piece by piece, it lets an AI tool point to an existing, pre-built interface, one of a set of ready-made widgets such as a dashboard, a form, or a map, and have it show up right inside the conversation. Teams build these with an SDK made for the standard, rather than custom, one-off code for each host, which is what lets a single interactive UI scale across many different AI tools. A growing number of MCP servers now support this, and it’s already built into tools like Claude and ChatGPT.

MCP Apps is close cousins with MCP-UI, an earlier open-source project that pioneered the same idea before MCP Apps became the shared standard. Different names, same goal: give the AI a known, trusted set of building blocks to point to, instead of a blank page to fill in.

None of this means every screen in a product should suddenly render itself. The better question for any team is narrower: does this specific part of the application UI actually need an AI deciding what to show, or would a well-designed, static screen do the job just as well?

A few signals point toward yes: a chat interface is carrying work it wasn’t built for, the desired output looks more like an object (a plan, a comparison, a dashboard) than a paragraph, or there’s already good data behind a feature that customers currently can’t act on directly. A few signals point the other way: if a flow has to look identical for every single user, or if a team can’t yet define what the AI is and isn’t allowed to generate, that’s a sign the constraints need attention before this becomes a product’s primary interface, not after. Settling that early saves a lot of rework later, whatever the feature grows into down the road.

Here’s where Punchcut’s own point of view on this comes in.

The exciting part of generative UI (an AI building the exact interface a moment calls for) is also the part that can go wrong fastest if nobody’s watching. Given too much freedom, an AI can drift off a brand’s look and feel, produce something inaccessible, or build an interface that sounds confident while getting the details wrong.

This is why Punchcut treats generative UI the same way it treats every other AI design problem: through the lens of Shared Autonomy, one of the firm’s core design principles, which is really just the balance between what a person controls and what the AI is trusted to handle on its own. The most usable version of generative UI isn’t the one that gives the model the most freedom. It’s the one where a design and engineering team has already decided what the AI is allowed to build, what it’s never allowed to touch, and what a person needs to check before anything reaches a real user.

That means starting with the guardrails, not the demo: a small, trusted library of components, a clear plan for what happens when the AI gets something wrong, and a review step before anything new ships. Generative UI should earn its flexibility. It shouldn’t be handed a blank canvas and trusted to figure the rest out.

Done well, generative UI has a real, positive impact on how people get things done with AI. It cuts out the back-and-forth of typing, reading, and re-asking that a plain chat exchange requires. Because it can adapt to a person’s actual context and needs, rather than a generic average user, it opens up real opportunities for better accessibility and a better fit for people a one-size-fits-all screen was never built for. It can also cut real development time on a team’s end, since the focus shifts from drawing every possible screen in advance to defining the intent and guardrails the AI operates within.

The risks are just as real. An interface that looks different every time someone opens it causes exactly the confusion you’d expect. Even Nielsen Norman Group, which has written extensively about this shift, points out that constantly changing interfaces create their own usability problems. Generated interfaces can also drift from a product’s look and feel, which is enough to frustrate people who expect something to behave the same way twice. Real-time generation isn’t free either: building a fresh interface for every request can take immense processing power, which adds cost and delay that a static screen never had to deal with. And AI models still hallucinate: they can produce an interface that looks polished and confident while still being wrong underneath.

None of that is an argument against generative UI. It’s an argument for building it the way Punchcut approaches any AI-driven interface: with real constraints, a clear sense of what the AI is and isn’t allowed to decide, and a person somewhere in the loop.

Most teams don’t need to solve every part of this on day one. A reasonable first step is picking one task where the current chat-only experience is falling short, then testing whether a small, well-designed interface (a handful of cards, one form, one comparison view) helps someone get through it faster and with more confidence than plain text did.

Punchcut works with product, design, and engineering teams on exactly this kind of work: figuring out where a chat interface has outgrown its usefulness, and shaping a working prototype with real guardrails in place, not an open-ended demo that falls apart the moment real users touch it. Browse recent work, read more in Insights, or get in touch to talk through where generative UI might fit your product, now or in the future.