Preparing our web architecture for AI Agents

When someone asks ChatGPT, Perplexity, or Google SGE a question about your industry, an AI agent goes out to search for the answer on the internet. That agent reads web pages, extracts information, compares options, and writes a response. Your company may appear in that response—or it may not. It all depends on how well your website is optimized for machine readability.


And this doesn’t just happen with search engines. AI agents in the B2B world are already performing more complex tasks. These agents can research suppliers, compare proposals, verify data, and write summaries for procurement teams. An agent from a company might be analyzing your offer and comparing it to three competitors without a real person having visited your website yet. What that agent finds—and how it interprets the information—will determine whether your company is included in the conversation or left out.


I wrote this article for teams that want to understand what this means in day-to-day operations. I want to explain what changes you need to make to your website’s architecture, why it’s so important to get it right, and how you can get started without having to scrap everything you’ve already built.

How AI Agents Are Transforming the Customer Experience

What exactly is an AI Agent?

An AI agent is a system that can read information, reason about it, and take autonomous actions to achieve a goal. It’s not a chatbot that answers questions in a pop-up window; an agent can browse websites, read documents, fill out forms, compare options, and interact with APIs—all without a human directing every step.

ChatGPT with web browsing, Perplexity, Google SGE, and a growing list of business assistants work exactly this way. When someone asks their assistant, “What software providers are available for medium-sized businesses in Latin America?”, the agent goes out, browses, reads pages, extracts information, and generates a response. Your site may or may not appear in that response. And if it does, it may be represented accurately or with errors, depending on how machine-readable it is.

What makes this particularly relevant for B2B companies is that these agents are no longer just searching for information—they’re executing parts of the buying process. They research, compare, verify availability, and draft executive summaries for decision-making teams. A company’s procurement agent may be evaluating your offer, comparing it with three competitors, and preparing an analysis for the chief operating officer—without anyone in your company even knowing that this process is taking place.

A Strategic Perspective on the B2B Model

The problem is that websites are designed for human eyes

Most modern websites are built with the visual experience in mind: animations, drop-down menus, content that loads dynamically with JavaScript, images with embedded text. All of that works perfectly for a person using a browser. For an AI agent, many of those elements are real obstacles.

If the most important content on your page depends on JavaScript running to load, many crawlers will never read it. If your success stories are in PDFs without selectable text, or your services are described primarily through images, or your videos lack transcripts, that information simply doesn’t exist for an agent. From its perspective, that page is empty.

There’s another, more subtle problem: ambiguity. Humans infer meaning from visual context—they see a logo, a color, a photograph—and build a mental image of the company. An agent needs that meaning to be explicitly stated in the text. If you have a section titled “Solutions” with creative product names but no clear descriptions of what each one does or who it’s for, a human can intuit what it’s about. An agent cannot. And what it doesn’t understand, it doesn’t include in the response it gives to its user.

The good news is that this doesn’t require redesigning everything from scratch. The visual experience for human users can remain the same. What needs to be added is a layer of machine accessibility: structured content, correct semantic tags, and explicit data that doesn’t rely on visual inference. On the site’s most critical pages, this sometimes also involves rethinking how information is organized so that it’s easy to extract.

Let’s consider a specific example. A technology services company has a “Success Stories” page with images of clients, logos, and a generic paragraph describing results below each one. To a human, it looks fine. To an AI agent, that page probably doesn’t convey anything useful: the client names are in images, the results are vague, and there’s no structure that allows the AI to extract information about which industry it was, what problem was solved, or what metrics were achieved. That content won’t appear in any AI response about success stories in that industry, no matter how impressive the work it describes may be.

What an AI agent needs to properly read your site

Clean HTML and correct semantics

The starting point is that the most important content should be available in HTML that any crawler can read directly, without relying on JavaScript to render it. This doesn’t mean removing dynamic interactions from the site; it means that the essential information—who you are, what you offer, who you serve, and use cases—must exist in the page’s source code and not be loaded only when the user interacts with a specific element.

Semantic markup matters more than it seems. Using headings (H1, H2, H3), lists, description tags, and alt attributes on images correctly isn’t just good web accessibility practice—it’s what tells a crawler how the information is organized and which parts are most relevant. A site with good HTML semantics is much easier to process than one where everything is nested inside generic `div`s without hierarchy or context.

Schema markup: data that machines can understand directly

Schema markup is a standard that allows you to tag a site’s content so that crawlers understand exactly what each element is about. Instead of a crawler having to infer that “Pedro Rojas, CEO is the company’s primary contact, schema explicitly and unambiguously declares this. It’s like adding descriptive tags to every piece of information on the site—not for users to see, but for machines to read.

For B2B companies, the most useful schema types are Organization (company information, contacts, locations), Product or Service (description of what’s offered), FAQPage (structured frequently asked questions), and Article (blog content or resources). Implementing them correctly increases the likelihood that an agent will interpret your information correctly and use it accurately when generating responses for its users. And what’s most interesting is that many of these schema types also improve how your site appears in traditional search results, so the benefit is twofold.

Accessible and documented APIs

The most sophisticated agents don’t just read pages; they also interact with APIs. If your company has a product catalog, a customer portal, or a quoting system, providing a well-documented API allows agents to interact with your platform efficiently and seamlessly. If an agent can check availability or initiate a quote directly, the purchasing process becomes smoother for the customer.

In B2B, this is especially relevant because companies that buy from other companies are building automated workflows to manage suppliers and compare options. If your platform doesn’t have an accessible API, it simply can’t participate in those workflows. And over time, that will weigh increasingly heavily on purchasing decisions.

Speed and technical stability

This is no different for agents: a slow or unstable site will be crawled incompletely. AI crawlers have time and resource limits, just like those of traditional search engines. Fast load times, stable servers, and clean URLs without unnecessary redirects remain part of the technical foundation that makes everything else work.

There’s one additional point that’s sometimes overlooked: consistency. If the same information appears in different forms on different pages of the site, or if there’s outdated content that contradicts more recent information, the crawlers may get confused or use incorrect data. Keeping your site up-to-date and consistent isn’t just good practice; it’s part of making a good impression on this new type of visitor.

SEO is no longer enough—AEO is needed

For more than two decades, SEO was the central discipline for appearing when someone searched for something relevant. The standard approach was to optimize for Google, appear in the top positions, and let Google connect your business with its audience.

That logic still holds true, but it’s no longer enough. What many are calling AEO—Answer Engine Optimization—is emerging. The difference is significant: in traditional SEO, the goal is to appear in the results so that a human will click on your link. In AEO, the goal is for your content to be the source that an AI agent uses to generate a direct response—often without the user ever visiting your site.

In B2B contexts, where value lies in being recognized as an authority and appearing in decision-making conversations, being the source cited by an AI agent carries enormous weight—even if that user never clicks on your link.

This implies a specific shift in how content is written: it should be more focused on answering specific questions, with verifiable information and a structure that allows for the extraction of precise answers. A services page that directly answers “What kind of company is this service for?”, “What problem does it solve?”, and “How long does it take to implement?” is much more likely to appear in an AI response than one that talks about “innovative solutions for organizations seeking to transform themselves.” The former is useful for a machine, whereas the latter no longer convinces even humans.

Blog content also plays an important role. Articles that answer specific questions—with titles that match what people are actually searching for and containing verifiable information—become natural sources for agents. The urgency to get this right increases when the reader might be a machine that decides in a matter of seconds whether your information is clear and reliable enough to use.

This isn’t an excuse to write dry or robotic content. It’s an invitation to write content that’s useful, that answers real questions, that’s honest about limitations and context, and that provides concrete information rather than generalities. That kind of content works for both humans and machines, and it’s what builds real authority across any channel.

The SEO Your Business Needs to Rank Well in AI

 

 

The aspect no one wants to ignore: privacy and governance

Preparing your website for use by AI agents isn’t just a good idea. It also raises questions you need to address before problems arise.


What data do you want AI agents to use freely, and what data should be protected? How can you tell which AI agents are accessing your site and why? What happens if an AI agent scrapes your prices or product catalog and passes them on to a competitor?


These questions are already a reality, and many companies are facing them without having a plan in place. Using robots.txt and llms.txt helps, but it’s not enough. You also need to update your site’s terms of use to make it clear what can be done with the content automatically. It also helps to set rate limits on APIs to prevent abuse and monitor whether incoming traffic appears to come from real people.


There’s another very important point: the quality of the information that AI agents will read. If AI agents read outdated prices or service descriptions that are no longer accurate, they’ll provide false information to their users. It’s vital that what your site says, what your API says, and what you sell are exactly the same. Machines don’t realize when something doesn’t make sense, and they won’t ask you for clarification. You also need to look at traffic differently because not all AI agents have good intentions. There are programs that simply crawl websites to steal data for no reason at all. You need to be able to distinguish between AI agents that help you and those that just drain your resources or steal your information without permission.


Most of these things aren’t difficult if you plan for them from the start. The problem is that people almost always ignore them until an error occurs, and then they have to fix it quickly with no other options.

Where to start

The task may seem like a complete redesign, but it almost never is. It’s about making small adjustments that improve machine accessibility without affecting the human user experience.


The best place to start is with a technical audit focused on machine-readability: which content relies on JavaScript, which pages have schema markup, how the HTML is structured on the most important pages, and whether there’s technical documentation for integrating with your systems. Tools like Screaming Frog or Semrush can quickly highlight these structural issues.


The most valuable pages are the first ones worth working on: the homepage, service pages, and success stories. These pages receive the most traffic, answer the most questions, and benefit the most from improved semantic structure and schema markup. The blog, if well-written with specific questions and direct answers, becomes a natural resource for agents researching the industry or comparing providers.


Decisions regarding APIs require strategic discussion: what information is shared, under what conditions, and with what level of control. Not all companies need to resolve this immediately, but in B2B—where integration with clients’ systems is almost always part of the equation—having it figured out before they ask for it gives you an advantage. By the time clients ask for it, it’s already too late to design it properly.


A useful way to prioritize is to ask yourself: If an AI agent visited your site today looking to understand what your company does, for whom, and why it should consider you, what would it find? Could it read your content? Would it clearly understand your value proposition? Would it find structured contact information? The answers to these questions are the clearest guide to knowing where to start.

Conclusions

AI agents are already browsing the web, reading pages, comparing options, and generating recommendations on behalf of their users. The gap between sites that are ready for them and those that aren’t will continue to grow.

Getting ready doesn’t mean starting from scratch. It means understanding how this new type of visitor works, what it needs to properly read your site, and making the right adjustments to your site’s architecture, content, and governance. Some adjustments are technical and relatively quick: schema markup, semantic HTML, llms.txt, and reviewing JavaScript dependencies on key pages. Others are more strategic and require business decisions: which APIs to expose, how to structure content to answer direct questions, how to maintain consistency of information across channels, and what permissions to grant to the different types of agents visiting the site.

None of these steps is impossible. What they do require is for someone to put them on the agenda, assign an owner, and follow up on them. The companies that are doing this aren’t necessarily the largest or those with the most robust technology teams; they’re the ones that understood sooner than their peers that the rules of the game are changing.

The companies that get this right first will have a real advantage: not only in terms of visibility within AI systems, but also in their ability to integrate into the workflows their customers are already building. Your website has always been the public face of your company. What’s changing is who—or what—is looking at it. And that deserves your attention right now.

How does an AI agent see you today?

If you don’t have an answer to that question, that’s the first step: finding out. A basic technical audit can quickly reveal the most significant gaps: content that crawlers can’t read, pages without schema markup, inconsistent information across channels, and a lack of documentation for integrations. Once that’s clear, the priorities become obvious.