For years, websites were built primarily for two audiences: users and search engines. Content had to be legible to humans, and site structure understandable to Google.
AI agents introduce an entirely new way of interacting with the web. They can find information about a business and use it in a reply, but increasingly, they can also perform actions on the user's behalf. Checking an available date, pulling a live price, finding a product that fits specific requirements, preparing an estimate, or checking order status.
This demands more from a website than good copy and proper SEO. Public information must be easy for AI systems to locate and understand, while processes meant to be handled by agents need secure, clearly defined interfaces.
The early building blocks of this infrastructure are already here. Formats like llms.txt are emerging, the Model Context Protocol is evolving, and CMS platforms are starting to describe their features in ways agents can use. WordPress is a good example, with its ongoing work on the Abilities API.
This doesn't mean every assistant can automatically run operations on any website just yet. However, this direction is well worth factoring in when designing new sites, shops, and web applications.
An AI agent can do more than just read a page
The simplest scenario is already familiar. An AI system scrapes publicly available content and tries to determine what a business does, what services it offers, where it operates, and what its terms of service are. In this case, what matters most is the quality, structure, and accessibility of the information published on the site.
A more advanced scenario begins when the user expects a specific action to be carried out. Instead of visiting several hotel booking sites manually, they might ask an agent to find an available room meeting specific criteria. Instead of clicking through a product configurator, they might give the agent parameters and ask for a price check.
Completing a task like this takes more than reading text on a page. The agent needs live data or access to a specific operation exposed by the underlying backend system.
Website content is still the foundation
Getting a site ready for AI agents doesn't start with installing extra tools. The first step is checking whether the information a user might look for is readily available in a clean, legible format at all.
Service descriptions, scope of work, pricing, terms of service, contact details, and answers to common questions should be standard on-page copy. Essential information shouldn't exist solely inside images, PDF documents, or elements that require complex browser interactions to view.
Clean document structure, logical headings, unambiguous service naming, and appropriate structured data also play an important role. Much of what has been considered best practice in technical SEO and digital accessibility for years remains just as valuable when the content consumer happens to be an AI system.
llms.txt can point an agent to your core content
An additional layer can be llms.txt. This is an emerging proposed standard designed specifically for systems powered by language models.
A file placed at /llms.txt can contain a brief description of the website along with curated links to the resources most relevant for an external system. For a corporate website, that might include service overviews, pricing, documentation, FAQs, terms of service, or contact details.
Here is what an example file might look like:
# Example Company
> We design and develop e-commerce platforms and web applications.
## Services
- [E-commerce development](https://example.com/services/ecommerce.md): Custom online stores and e-commerce integrations.
- [Web applications](https://example.com/services/web-apps.md): Custom web applications and business systems.
## Company
- [About us](https://example.com/about.md): Information about the company and team.
- [Contact](https://example.com/contact.md): Contact information.
## Resources
- [FAQ](https://example.com/faq.md): Frequently asked questions.
- [Knowledge base](https://example.com/knowledge-base.md): Technical articles and guides.
llms.txt doesn't need to contain the entire content of the site. It can act as a lightweight map pointing to key resources and their plain-text Markdown versions.
In practical setups, you might also encounter llms-full.txt, which consolidates most of the site's content into a single document. For a smaller site, that can be convenient, but as the page count grows, one massive file becomes impractical. Splitting materials into distinct documents allows the agent to fetch only the specific information it actually needs.
llms.txt does not replace your website or SEO
Adding llms.txt shouldn't be treated as a new version of SEO or a magic shortcut to boost your visibility in AI-generated answers.
The format does not replace public HTML copy, robots.txt, sitemaps, structured data, or sound information architecture. Nor does it grant agents the ability to execute operations in your backend.
Its role is much more straightforward: it indicates to an AI system where key materials live and serves them in a clean format ready for downstream processing.
There is also no reason to treat the presence of llms.txt as a Google ranking factor. It is simply an extra layer designed for systems that choose to support the format.
Markdown can streamline access to content
An agent doesn't always need the full markup of a page along with its navigation, footer, scripts, forms, and UI components. In many cases, all it needs is the raw body text of the document.
That is why llms.txt can point to stripped-down versions of pages stored as Markdown. At a URL like:
https://example.com/services/ecommerce.md
you can serve the title, service description, scope of work, terms, and other essentials without any layout clutter.
This doesn't mean you need to maintain two manual copies of every page. In a well-architected system, HTML and Markdown can be generated from the exact same content source, so an update in the CMS keeps both representations in sync automatically.
Static content cannot serve dynamic data
Service descriptions, terms of service, and company background change relatively infrequently and work well as static content. Booking availability, stock levels, live prices, or order statuses are a completely different story.
This type of information depends on the real-time state of your system. Putting it into llms.txt or a static Markdown document solves nothing, because the data can become stale within seconds.
This is where an API is needed. The agent can send query parameters, and the system returns a live response without having to scrape it from a user-facing interface.
A service business can expose open booking slots this way, an online shop can return live inventory, and a configurator can calculate a product price based on user-supplied specs.
APIs are no longer just for apps and integrations
Historically, a website or store API was built for a single specific consumer: a mobile app, an ERP system, a warehouse integration, a customer portal, or a partner connection.
AI agents are poised to become another standard consumer of these interfaces. But they need more than just an endpoint URL. They also need to know what operations are available, what parameters to provide, what response format to expect, and what permissions are required.
Being able to describe system capabilities in a machine-readable format is quickly becoming critical. That is where solutions like MCP and native platform mechanisms come in.
MCP can connect an agent to your business systems
The Model Context Protocol allows you to expose tools to agents in a standardised way. A tool can be a simple read operation or an action that executes a specific workflow inside the system.
A company can give an agent a strictly scoped set of capabilities without granting access to the entire admin panel or opening direct database connections.
Example operations might look like this:
check_availability
get_product_price
calculate_quote
check_order_status
create_lead
The agent receives a definition of the operation and its parameters. When a user asks for a price quote, the model collects the necessary details, calls the corresponding tool, and uses the returned result in the ongoing conversation.
WordPress is starting to build a similar layer
A clear sign of where things are heading is WordPress. Starting with version 6.9, it has been developing the Abilities API: a central registry of operations available within the system.
Each ability can define a name, description, input and output data schema, an execution callback, and permission checks. A booking plugin can use this to expose date availability checks, and a product configurator can expose pricing logic.
Example abilities might look like:
booking/check-availability
products/calculate-price
orders/check-status
leads/create
WordPress core currently exposes only a modest set of baseline abilities around site info, users, and environment. The real value of this mechanism lies not in what is bundled out of the box today, but in the ability for plugins and custom code to register their own abilities.
WordPress is just one example here. The same pattern can be applied to an application built in Laravel, a custom e-commerce platform, a SaaS product, or an existing company API.
Content, llms.txt, APIs, and MCP serve different purposes
Preparing a website for AI agents isn't about picking one single standard. Each approach tackles a different way of interacting with the site, and they work best in tandem.
| Layer |
Purpose |
Example |
| HTML |
Public content for users, search engines, and AI systems |
Service descriptions, pricing, company background |
llms.txt |
Pointing to key documentation and resources |
List of services, FAQ, and docs |
| Markdown |
Clean, streamlined representation of content |
/services/ecommerce.md |
| API |
Fetching live data and running operations |
Checking price or availability |
| MCP |
Exposing operations as structured tools for agents |
calculate_quote |
In practice, an agent might first discover a service through public HTML or llms.txt, read its details via Markdown, and then invoke an API or MCP tool to pull real-time pricing or check booking availability.
Readying a site for agents does not mean migrating all your content into endpoints. Company overviews, service scopes, pricing guides, and terms should remain regular, crawlable public content.
An API makes the most sense where the output depends on real-time system state or involves business logic. Booking availability can change multiple times a day, a configured price might depend on a dozen parameters, and order status is customer-specific data.
Likewise, llms.txt should not be stuffed with a mirror of the entire site just because it is technically possible. It is far better used to point to key sources, keeping actual data in the place that best matches its nature.
Agent-driven actions require strict permissions
Reading a public service description carries zero risk. Creating a booking, updating an order, or querying customer data requires proper security controls.
An agent should only ever receive the minimum permissions needed to complete a specific task. An integration should never rely on an administrator account simply because it was the easiest way to access the API.
In practice, use dedicated credentials for each integration, enforce the principle of least privilege, validate input parameters, and log every operation. When an action mutates data, it is equally important to define which steps require explicit user confirmation.
For instance, an agent could independently check appointment availability, but placing a paid booking should require explicit sign-off from the user.
What does an AI-ready website look like?
In most cases, you don't need to rebuild your site from the ground up. The main task is bringing structure to layers you already have.
Public copy should be accessible and clear. Core materials can be signposted via llms.txt and served in lightweight Markdown. Real-time dynamic data should flow through an API, and agent-executable business processes can be exposed as secure tools.
Depending on the type of business, these might include:
- checking appointment or booking availability,
- fetching live product prices,
- checking stock levels,
- generating price quotes based on custom parameters,
- checking order or ticket status,
- creating a lead or quote request,
- reserving a date after user confirmation.
Not every business needs all of these capabilities. The greatest ROI comes from exposing processes that are already performed by customers on the site or handled manually by your team.
What can you do right now?
The first step is auditing your public content. Check whether your services, pricing, terms, locations, contact info, and most frequent inquiries are available as plain text that can be interpreted without ambiguity.
Next, you can set up an llms.txt file pointing to your primary resources and, where appropriate, publish streamlined Markdown versions of those pages.
The next phase is reviewing the backend workflows behind your site. Good candidates for integration have well-defined inputs and outputs: checking dates, calculating rates, or looking up a status.
If these processes already have an API, part of the foundation is ready. If the logic is locked inside a form, an admin dashboard, or app code, you can build a dedicated interface for it. MCP or tools like the WordPress Abilities API can then describe these endpoints in a format built for agents.
Websites will be built for more than humans and search engines
It remains to be seen what share of traffic, inquiries, and transactions will eventually run through AI agents, or which emerging standards will gain widespread adoption. There is no sense in overhauling an entire site just to slap an "AI ready" badge on it.
What does make sense is laying groundwork that delivers value regardless of how agent ecosystems evolve. Well-structured copy, clean site architecture, reliable APIs, tight permissions, and clear separation between public content and authenticated actions improve your system architecture today.
llms.txt, Markdown, and MCP can be introduced incrementally wherever they provide clear utility. That ensures your site remains fully effective for humans and search engines, while being ready for a future where users delegate tasks to their own AI agents.
When designing websites, online shops, and web applications, we can evaluate both your public content and the backend workflows worth opening up to agents. This applies to WordPress and WooCommerce, as well as custom apps, SaaS platforms, and bespoke e-commerce systems.
If you would like to assess your current site from this perspective, get in touch. We can review your content, APIs, and backend processes, pointing out the areas worth preparing for the continued rise of AI agents.
