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Ok, let’s get into it.

Think about every piece of software you've ever used.

The buttons. The colors. The onboarding flow that holds your hand. The pricing page nudging you toward the middle plan.

All of it built for human eyeballs. (…why would it NOT be built for human eyeballs?)

Twenty years of UX best practices, A/B tests, and conversion psychology. Every pixel placed to persuade a person sitting in front of a screen.

That person is starting to leave the room.

Here's what's already happening: people ask AI agents to compare tools, book services, fill out forms, and buy things for them. Claude browses. ChatGPT browses. Perplexity shops.

And when an agent lands on your product, it skips everything you spent months polishing.

Your hero section. Your countdown timer. That carefully tested shade of green on your CTA.

The agent asks one question: can I complete the task here?

If it can't, it leaves. And it leaves silently. No bounce to analyze, no exit survey, no complaint. It just moves to a competitor whose product it could use.

Now zoom out. Your customer's agent negotiating with a vendor's agent. A research agent pulling from twenty sites in the time it takes you to load one. Purchases happening between two pieces of software while both humans are asleep.

In that world, "good UX" means clean data, clear APIs, and pages a machine can read without guessing. Looking pretty stops being the job. Being usable by things without eyes becomes the job.

You'll still be in the room, don't worry. But you'll be setting the goals while agents click the buttons. And no UX playbook covers that yet.

Open Claude or any agent that can browse. Give it a real task on your own site or product: "Find my pricing and tell me which plan fits a solo founder." Or "sign up for my newsletter."

Then watch.

Wherever it hesitates, gets lost, or gives you wrong informaiton, you're seeing a preview of how the next wave of your users experiences you.

Three moves, easiest first:

Add an llms.txt file. A plain text file at yoursite.com/llms.txt that tells AI models what your product does, what your pages contain, and where the important stuff lives. Think robots.txt, but for agents. Takes 20 minutes. There's a spec at llmstxt.org, or just ask Claude to write one from your homepage.

Clean up your buttons and forms. Agents navigate by reading your HTML, and most sites are a mess under the hood. Buttons that are actually images. Forms with unlabeled fields. Critical info trapped inside screenshots. If your "Sign up" button is a div with an onClick, an agent might never find it. Ask whoever built your site (or Claude Code) to run through it and label everything properly.

Build an MCP server. This is the advanced play. MCP (Model Context Protocol) is the standard that lets agents plug directly into your product: no clicking, no guessing, just clean access to your data and actions. Stripe has one. Cloudflare has one. If your product has an API, you're closer than you think. One weekend with Claude Code gets you a working version. Look for example at Attio, a perfect example of an agentic first CRM:

Get revenue agents, workflows, and automations across every stage of your motion. Access customer data in real time through Attio's web app, MCP, API, and SDK.

Then Ask Attio anything about your business and get instant answers.

It's the CRM that runs the work behind every win.

Try Attio Now

Start with the first one today. The visitors who can't complain are the ones you'll never know you lost.

So stop asking "does my product look good?" and start asking "can a machine use it without me explaining anything?"

The second question is about to matter more.

What would an agent say about your product today?

Stefano Martell
Co-Founder, The AI Leverage by Bestapps.ai

ps: Grab your Bestapps Academy founding member spot here https://www.skool.com/bestapps-academy-8162/about