A website now has more than one kind of visitor. A person may compare services, examine evidence, and contact a business. A search system may extract an answer. An AI assistant may gather information on someone’s behalf. A connected agent may request a quote, look up an order, or prepare a transaction.
That does not mean replacing the website with a chatbot. It means making the same underlying information and workflows understandable, reliable, and appropriately controlled for each visitor. The route there is familiar: clear writing, semantic pages, accessible controls, maintained data, and explicit rules for consequential actions.
The machine-facing website is already here
Google’s guidance for AI search features says the familiar foundations still apply: pages must be accessible to Search, eligible for indexing and snippets, and useful to people. Google says there are no additional technical requirements or special optimizations for appearing in AI Overviews or AI Mode. Eligibility does not guarantee inclusion.
That is one kind of machine interaction: software reading public pages. Another is an agent using a defined tool to do something. The Model Context Protocol specification describes tools that connected applications can expose to models. Its guidance calls for clear tool visibility and human oversight, particularly when a tool performs an operation. A readable page and an executable action solve different problems.
WordPress offers an instructive example. Its Abilities API, available in WordPress 6.9 and later, lets developers register functions with defined inputs, outputs, and permissions. The separate WordPress MCP Adapter can expose registered abilities to compatible agents. That does not make every WordPress site ready for agents by default. Someone still has to define useful abilities and govern access.
A custom application can expose similarly bounded actions through its own API. The platform choice matters, but the design question comes first: what should a visitor be able to understand or accomplish, and who is allowed to act?
A four-layer test for a useful website
Before adding an AI interface, examine four layers.
1. Meaning
Can someone tell what you offer, who it serves, what it costs or depends on, and what happens next? Put those answers in visible copy with descriptive headings. A product page should distinguish an available item from a custom-order inquiry. A service page should explain scope and limits. Avoid hiding essential facts in a slideshow, image, or chat-only response.
2. Evidence
Can a reader follow a claim back to a named source, method, policy, or real example? Date time-sensitive guidance. Explain what an example demonstrates and what it does not. If the evidence is weak, tighten the claim. This makes the page more useful to a person deciding whether to trust you and to any system trying to summarize it.
3. Interaction
Can a person complete the task with a keyboard, understand errors, and recover from a mistake? WCAG 2.2 addresses matters such as visible focus and usable target size. These are not decorative details. If an interface is hard to navigate or ambiguous, adding an AI assistant does not repair the underlying journey.
4. Action
If an agent can do more than read, what is the narrowest action it needs? “Find the shipping policy” is different from “change the shipping address.” Define the caller, allowed inputs, permission check, confirmation step, audit record, and failure response. Treat an agent’s request as a request to the application, subject to the application’s rules.
The layers build on one another. A tool cannot safely automate a business process whose rules have never been written down. An assistant cannot give a dependable answer when the source page contradicts the policy document.
Consider a purchasing workflow
Illustrative scenario, not a client result: A manufacturer sells a small catalog to approved business customers. The site displays product specifications and purchasing terms. A signed-in customer can reorder an item, subject to account pricing and inventory.
A poor implementation would let a general-purpose assistant infer the correct SKU from a conversation, fill a checkout form, and submit the order. It may choose the wrong packaging unit, overlook a purchase-order requirement, or present an outdated price.
A better sequence separates jobs. Public pages explain specifications in clear, maintained language. An authenticated lookup returns the customer’s permitted products and current terms. A narrowly defined draft-order action accepts validated SKU and quantity values. The application calculates pricing and availability. A human reviews the draft before final submission. Each step has an identifiable source of truth and a clear owner.
None of this requires a particular front-end framework. A conventional WordPress and WooCommerce build, a headless system, or a custom application could implement portions of the workflow. Their maintenance burden and integration options differ. The decision should follow the actual operating requirements.
Structured data is a label, not a substitute for substance
Structured data can clarify what a page represents, such as an article, organization, product, or service. Google recommends that markup describe the page it appears on and match information visible to readers. Its structured data guidance also warns that valid markup does not guarantee a rich result.
For an article, the sensible order is to publish a useful article, show an accurate byline and date, and then mark up those same facts. Adding a large schema graph to a thin page does not create expertise. Neither does inserting a list of questions merely to obtain FAQ markup.
The same discipline applies to claims about AI visibility. Make important pages crawlable where appropriate. State answers clearly. Link related explanations. Keep facts current. Measure actual discovery and useful inquiries. Do not promise that a particular chatbot will quote the page. See our AI search visibility perspective for the technical foundations.
When an agent should stay out of the way
Some tasks need no agent. A searchable knowledge base may be more dependable than a conversational wrapper. A form with clear options can beat a bot that asks ten questions to reach the same destination. A deterministic rule is preferable when the decision is stable, auditable, and easy to express.
An agent becomes more useful when the request spans several sources, the wording varies, or a person needs help preparing a complex action. Even then, the system should show the information it used, the action it proposes, and the point at which a person must take over. That is the starting point for custom AI tools, regardless of the website platform.
Our view: the winning website is unlikely to be the one with the most AI features. It will be the one with the clearest promises, the least confusing paths, and the best boundaries around automation.
What to do this quarter
Pick one high-value journey: finding a service, comparing a product, requesting support, or placing a repeat order. Ask five questions:
- What must a person know before deciding?
- Where is the authoritative, maintained answer?
- Can the journey be completed accessibly without an AI assistant?
- Which step, if any, benefits from an agent?
- What permission and confirmation are required before that agent acts?
Fix the first three answers before exposing a new tool. Prototype the fourth against realistic examples. Test the fifth with mistakes, expired information, missing permissions, and an interrupted session. Website development and automation should both start with the journey people need to complete.
The web is gaining new interfaces. Its most valuable job remains the same: help someone understand enough to make a sound decision and complete the next step with confidence.