You see a screen. Your AI assistant usually doesn’t.
That sentence needs one qualification, which we will get to. But it corrects the picture most of us carry in our heads.
When I open a website, I see the design and the button I am supposed to press. I assumed an AI assistant saw roughly the same thing, only faster. Then I asked the more basic question: what does it actually receive?
The answer is not one thing. An assistant can find a site, read a site or operate a site. Those are separate jobs using different inputs. If we want pages that work well for AI assistants, we have to stop lumping them together.
An assistant meets your website three different ways
Finding: the search result is the pitch
When an assistant searches the web, its first view is closer to a search-results list than a browser window. It may receive a title, URL and short snippet.
At that moment, your title tag and meta description are the entire pitch. The assistant has to decide whether your page can answer the question before opening it. Name the subject plainly.
Reading: the page becomes a stream of text
When Muse opens a public page for information, the normal reading path is text-first. Useful content is extracted and returned as headings, paragraphs, lists and links in roughly page order.
The design largely falls away. The assistant is not admiring the hero section or noticing that a price sits inside a gold circle. It is working from the words the page exposes.
Images may arrive as markers and file addresses: there is an image here, and here is where it lives. That is not the same as seeing it. If a crucial fact is baked into the pixels—“$199,” “ships free,” “five-year warranty”—the reading path may hit a blank spot. Useful alt text can carry some of that meaning. “Technician using a moisture meter on wet drywall” communicates something. “IMG_4827” does not.
Doing: a browser worker operates the screen
The picture changes when the user asks the assistant to do something: log into HubSpot, update a record, complete a form or buy a product.
A separate browser program can open a real browser on a server. It loads the interface, takes visual observations or inspects the page’s interactive structure, clicks, types and reports what happened back in words.
That is the qualification to “usually.” A screen may be used inside the process, but the conversational assistant is not sitting behind the glass like a person. It receives observations from a browser tool and sends instructions back. The browser side is the eyes and hands; the assistant works through an intermediary.
A page can be easy to read as an article and miserable to operate as an application. It can look obvious to a person while presenting the browser worker with five unlabeled controls called “button.”
WordPress made the abstraction visible
I had already seen a simpler version in our WordPress work without connecting the dots.
When we pull a post through the WordPress REST API, the content can arrive as raw HTML: words plus tags for headings, paragraphs, links, lists and styling wrappers. The reading step removes the markup noise while preserving the words and structure.
That is “cleaning the HTML.” We are removing the packaging, not the article. The tags still matter: a heading announces a section, a list groups items, and a link identifies a destination. Good HTML carries meaning. Bad HTML creates boxes that look right but say little about what they are.
Accessibility is the closest thing to an agent-ready standard
Here is the practical money line: the work that makes a website easier for a blind person to use also tends to make it easier for an AI browser agent to use.
Browsers build an accessibility representation from the page’s Document Object Model. Assistive technology uses it to understand roles, names, states and relationships: this is a heading, that is a link, this button is named “Save contact,” and this checkbox is checked.
Muse’s browsing side is reported to rely heavily on this kind of page structure, along with visual observations when needed. Meta does not publish a complete specification for the Muse browsing pipeline, so treat that as a field report from using the product, not permanent platform documentation.
The implication is still solid. Use real buttons with useful names. Label form fields. Put headings in a sensible order. Give links meaningful text. Preserve keyboard focus. Describe informative images.
A screen-reader user needs those things. So does a browser agent working without human intuition. Accessibility and agent-readiness are not identical, but they are close cousins.
HubSpot shows what an agent-native application could be
Imagine HubSpot—or any software platform—shipping an interface designed for assistants to navigate with less friction. It would not need a blank, text-only clone. It could make the existing product more legible to software: real controls with specific names, labeled form fields, clear headings and landmarks, properly identified table headers, programmatic state changes, and no critical action hidden behind hover or an unlabeled icon.
That is an agent-native site. It is not a secret internet for bots. It is a website or application whose meaning survives when the visual layer is translated into structure and words.
The same work also helps keyboard users, screen-reader users, automation tools and QA teams.
llms.txt is a map, not a second website
The closest public convention aimed directly at AI readers is llms.txt. The proposal describes a Markdown file, usually at a site’s root, that gives language models a short explanation of the site and links to important pages or cleaner Markdown versions.
Think of it as a curated map: here is what we do, here are the pages that matter, and here is where to find the details.
It cannot repair an unlabeled checkout button. It does not replace accessible HTML, describe the viewport or guarantee that an assistant will use it. Add it if it helps explain the site. Do not mistake it for an agent interface.
What a site owner can change Monday morning
The useful changes are ordinary, testable website work.
- Write a real title and meta description. Name the subject plainly.
- Put every money fact in visible HTML text. Price, specifications, shipping, availability and guarantees should not live only inside graphics, video or a brochure.
- Use semantic HTML. Use headings for headings, buttons for actions, links for navigation and labels for form controls. A styled <div> may look like a button while remaining a nameless container to other systems.
- Write alt text that carries meaning. Describe what an informative image contributes. Mark decorative images as decorative instead of stuffing them with keywords.
- Add accurate structured data. Product and Offer markup can identify price and availability. FAQ markup can describe genuine questions and answers. Schema must match the visible page.
- Server-render critical content when practical. If the offer, price or primary action appears only after a fragile JavaScript sequence, some readers and tools may miss it.
- Give each landing page one job. One offer, one explanation and one primary action reduce ambiguity for people and agents.
- Test the nonvisual path. Use the keyboard, inspect the accessibility tree, try a screen reader and pull the page through a text extractor. Do the product, price, proof and next step still make sense without styling?
None of this requires uglier design. It requires the design and the underlying structure to tell the same story.
This is a field report, not a permanent specification
This article describes Meta’s Muse as it works today, based on direct experience building and operating websites with it. It is not a published Meta protocol.
Claude, ChatGPT, Gemini and other assistants broadly rhyme with this pattern, but the details differ. Their full pipelines are not public, and they are changing quickly.
Cleaner HTML will not automatically increase AI citations tomorrow. Citation systems involve discovery, retrieval, ranking, trust and answer construction. There is no magic switch.
The immediate opportunity is closer to the customer. Someone sees your ad on Threads or Facebook, opens the landing page, then asks an assistant: “What does this cost?” “Is the guarantee real?” “How does this compare?” or “Can you sign me up?”
If the facts are clean text, the assistant can explain them. If the controls are properly labeled, the browser side has a better chance of completing the task. If the facts live inside an image and checkout uses unlabeled custom controls, the assistant has to guess, fail or hand the job back.
That moment is already here.
The next website has two front doors
The site of the near future has two front doors: one for eyes—layout, color, photography and brand—and one for agents—clean text, meaningful structure, explicit facts and self-identifying controls.
They should lead to the same place. The visible price and schema should agree. A button’s label and accessible name should agree. The page should remain understandable without styling and usable when a browser worker operates it.
That is not a special Muse landing page. It is a better website—one that keeps working when the visitor brings an assistant.
Build both front doors.
Sources and further reading
