Designing for Agentic AI: The Next Interface Challenge for Brands

The interface used to wait for you. You clicked, dragged, typed — and the system responded. That relationship is changing fast, and if your brand runs any kind of digital product or service, the shift is worth understanding now rather than after your competitors do.

The term getting thrown around is agentic AI — systems that don’t just answer questions but take actions on a user’s behalf, often across multiple tools, without step-by-step instruction. Think less “chatbot” and more “co-worker who can also operate your software.” As Aurélie Radom writes for UX Collective, AI coding agents already let you describe an outcome and let the system decide which commands to run, which files to change, which tests to execute. The familiar design problem that returns: what did the system understand, what is it doing, and what can I stop?

That question — what can I stop? — is the one most design teams aren’t ready for yet.

The blank prompt was only the beginning

The first wave of generative AI gave us empty input fields and asked us to describe what we wanted. That felt like a blank page problem, but it was actually something different. A blank page is hard because you have nothing to react to. A blank AI prompt is hard because it gives you no indication of what’s even possible. The system’s capabilities hide behind language. Users have to already know what to ask before they can discover what the tool can do.

That burden-on-the-user problem is now compounding. Agentic systems don’t just respond to a single prompt — they read context, reference upstream assets, and pass outputs from one step to the next. Radom’s piece points to Miro’s agentic canvas as a concrete example: it pulls context from Slack, GitHub, spreadsheets, and PDFs into a shared spatial environment, then acts on that context without needing step-by-step instructions. Figma is moving in the same direction, opening its canvas to third-party agents and building tools that let those agents understand design decisions and intent.

The design implication is significant. When the canvas can act — not just display — the designer’s job is no longer only about making the next step obvious. It’s about orchestrating what the AI does, when it acts, and how it communicates its intent back to the person nominally in charge.

Nielsen’s heuristics, which designers have leaned on for decades, still apply here. They just describe a harder problem. Visibility of system status now means showing what an agent has understood, what it’s currently doing, and where it is in a multi-step process. User control and freedom now includes the ability to interrupt, redirect, or undo an autonomous action mid-flight. Error prevention means deciding, in advance, what the agent is allowed to do on its own versus what must require a human decision. These aren’t new principles. The relationship they describe is new.

The fragmentation problem nobody’s talking about

There’s a second challenge underneath the UX one, and it has direct consequences for brand owners: there is no agreed standard for how agentic AI renders interface components across platforms.

Patrick Neeman, writing for UX Collective, lays this out clearly. When an AI agent needs to show a user a date picker, a form, or any interactive element, different clients handle it in completely different ways. Google’s Gemini Enterprise renders A2UI — a declarative JSON standard where the agent names components the client already holds, and the client draws them in its own styling. ChatGPT and Claude render sandboxed HTML through iframes, where styling is yours because you shipped it. Microsoft Teams uses Adaptive Cards. Slack uses Block Kit. Consumer Gemini writes generative UI fresh from the model each time. Grok renders nothing at all.

The result, as Neeman frames it, is a “surface tax”: every platform your brand’s digital experience touches is another target your design system has to serve, and every AI client multiplies that again. He draws the comparison directly to the browser wars of the late 1990s, when Netscape and Microsoft spent years shipping incompatible tags at each other, forcing developers to maintain two layouts and browser-sniffing code. That era ended when Chrome arrived and made rendering predictable — a consolidation Neeman cites as the model agentic UI needs to follow. Agentic UI is, as Neeman puts it, “the old browsers in a new costume.”

The standard that exists — Google’s A2UI, introduced in December 2025 — is still a release candidate. It’s declarative and vendor-neutral on paper, but as Neeman explains, it only reaches the clients users actually open if you ship its renderer inside every payload. That’s not a standard yet. That’s a workaround.

What this means if you’re running a brand’s digital presence

Here’s where we’d push back on the instinct to wait and see. Most brand owners aren’t building AI agents right now. But they are building digital products that will interact with them — and the decisions made in that design now will determine how much control a brand retains when agents start acting on their users’ behalf.

A few things are worth acting on today.

Transparency isn’t optional, it’s trust infrastructure. If your product or service integrates any kind of AI-driven action — automated emails, dynamic recommendations, form-filling, scheduling — users need to be able to see what the system did and why. Not buried in a log. Visible, in context, at the moment it matters. This is a design problem before it’s a policy problem.

Interruptibility is a feature, not an edge case. Agentic systems that can’t be paused or redirected mid-action will erode trust faster than they build it. Designing for graceful interruption — clear cancel states, confirmation gates for consequential actions, undo paths that actually work — is the kind of craft that separates thoughtful products from ones that feel out of control.

Your design system is about to face platform pressure. If your brand has a design system, the fragmentation Neeman describes means it needs to be built with surface-agnostic components in mind. If your design system is “whatever the developer builds for this sprint,” the surface tax will hit you harder than competitors who’ve done the foundational work.

This is also the moment to think seriously about what your brand’s digital experience is actually built on. Hand-coded, well-structured sites — the kind we build at Alkalyne’s web design and development practice — are easier to extend, audit, and adapt than bloated template stacks when new interface paradigms arrive. That’s not a future concern. It’s the current one.

The position we’d take

Agentic AI is not a feature to bolt onto an existing interface. It’s a different model of interaction that requires rethinking what the interface is for. The design work is no longer primarily about guiding a user through a flow — it’s about governing what an autonomous system does within that flow, and making sure the human on the other end can always tell what’s happening and take back control.

The brands that treat this as a UX checkbox will ship interfaces that feel unpredictable. The ones that treat it as a trust problem — and design accordingly — will build experiences that hold up as the technology matures.

The browser wars took five years to resolve. Agentic UI fragmentation is earlier in that arc. The time to develop opinions about it is now, not after a standard wins.