Beyond Chat Interfaces

Imagine this, you open your favorite mobile app. Maybe it’s where you keep your loyalty points, maybe it’s where you play quick games, or it’s where you manage your digital assets. For years, you knew exactly what to expect—buttons, menus, forms. You tapped, swiped, confirmed. But now something feels different. Instead of clicking through layers of menus, the app simply asks: “What do you want to do today?” You say it out loud, or type it in a single line—and suddenly the app is no longer a set of screens. It’s a partner, an agent, ready to plan, act, and deliver results on your behalf.

That shift—from structured interfaces to intent-driven ones—is changing how users behave, and with that, what they expect from design. Two recent explorations, one from Smashing Magazine on design patterns for AI interfaces, and another research paper on the “interface dilemma” in multimodal systems, both point to the same conclusion, AI is rewriting the rules of UI design

From Tapping to Asking

For decades, apps taught us to navigate through fixed flows: tap this, scroll that, fill here. But conversational and agent-driven interfaces flip the script. Instead of navigating a structure, users declare intent—sometimes in words, sometimes in voice, sometimes by selecting from smart suggestions.

This doesn’t mean prompts replace design. Quite the opposite, the new design challenge is to guide intent. Chips with examples, query builders, sliders, or even visual canvases are becoming just as important as traditional buttons. Interfaces are learning to help users say what they want instead of forcing them to learn where the app hid it.

Outputs That Act Like Answers

A text dump isn’t enough anymore. If you ask an app to recommend a game, it shouldn’t give you paragraphs—it should give you a ranked list, previews, maybe even a quick-play option. If you ask about loyalty points, you should see them as a timeline of transactions, not just a number. Good AI-driven design shapes the output to match the task: maps, comparisons, timelines, side-by-side versions. The UI becomes an interpreter, turning model output into something people can immediately act on

Refinement Becomes the Flow

Here’s the reality, the first answer is rarely the final one. That’s why modern patterns put refinement at the center. Highlight a part of the result, tweak it, re-run only that step. Switch tones, shorten or expand, bookmark versions. Instead of starting over with a giant new prompt, you sculpt the result. For products like superapps, where users jump between tasks—payments, games, social feeds—that ability to refine quickly is what makes the AI feel natural instead of frustrating.

When Chat Steps Back

Luke Wroblewski recently described how chat itself is slowly receding from the center of AI interfaces . At first, apps gave us a chat box and a scrolling thread—type, wait, type again—until you finally got what you needed. Then came split-screen layouts, one side for conversation, the other for reviewing outputs. Better, but still clunky.

Now agents are changing the picture again. Instead of negotiating every step with an AI, you give an instruction, and the system quietly does the back-and-forth on its own—choosing tools, calling other agents, adjusting its plan—until it hands you a finished result. The chat window is still there if you want to peek inside, but by default it stays hidden.

That’s an important shift. It means users won’t always expect to “talk” to their software. They’ll expect to start something, step aside, and come back when it’s done. And that’s a very different design challenge.

Agents in the Flow

We’ve all seen “AI tabs” added to apps, as if intelligence was just another feature. But the real change comes when AI meets users where they already are. An agent that books you a ticket, converts your loyalty points, or sets up a multiplayer match shouldn’t live in a separate chatbot—it should live inside the flow you already know. As Smashing Magazine put it, be AI-second, job-first. The action is primary, the intelligence simply makes it smoother.

Multimodal Means Many Interfaces

The arXiv paper calls this the “interface dilemma”. When models handle text, images, and voice at once, the “right” interface depends on context. A quick action might need a button, a complex task might need a structured plan, while voice might handle things where latency is low and hands are busy.

That’s where designers face the hardest choices. You’re no longer designing one interface—you’re designing a system of surfaces that adapt to task, context, and device. A loyalty app might need voice on the go, rich tables at home, and lightweight previews on a smartwatch. The design isn’t static, it’s situational.