For most of the web’s history, the model was straightforward.
A person used a browser.
A website responded.
Search engines helped connect the two.
That model is changing.
AI systems can now sit between the person and the website. They can search for information, retrieve pages, compare multiple sources and present a conclusion without requiring the user to visit every page themselves.
Some AI agents can go further. They can inspect an interface, understand controls and perform parts of a task on the user’s behalf.
Google now describes AI agents as autonomous systems that can perform tasks such as comparing product specifications or booking reservations. Its guidance also notes that browser agents can analyse screenshots, inspect the DOM and interpret the accessibility tree while using websites.Read Google’s official guidance on generative AI and agents
That introduces a fundamentally different idea.
Your website no longer has only a human audience.
It increasingly has a machine audience acting for humans as well.
That is the shift behind the agentic web.
What is the agentic web?
The agentic web is an emerging model of the web in which AI agents can discover information, interpret digital experiences and increasingly complete tasks on behalf of users.
It goes beyond putting a chatbot on a website.
And it goes beyond AI search.
AI search changes how people discover information.
The agentic web begins to change how people use the web itself.
Instead of manually visiting five websites to compare products, a user may ask an agent:
Find the best option for my requirements and explain the trade-offs.
Instead of navigating several screens to arrange a consultation:
Find an available slot next week and book it.
Instead of opening individual product pages:
Compare these models on price, warranty and compatibility.
The websites are still there.
But the immediate consumer of those websites may increasingly be software working on the user’s behalf.
That creates a new design question for every digital business:
Does the meaning of our website survive when the visitor is a machine?
The web is becoming multi-audience
Traditional websites were primarily designed around one experience: the human user experience.
Search added a machine-readable layer, but search crawlers did not usually need to use the website. Their job was largely to discover, process and index information.
Generative AI added another behaviour.
Google explains that AI Mode and AI Overviews can use query fan-out, issuing several related searches and retrieving information from multiple results before constructing a response.See how Google explains retrieval and query fan-out
Other AI platforms have their own retrieval systems.
OpenAI says publishers that want their pages eligible to be discovered, surfaced and cited in ChatGPT search should allow OAI-SearchBot access.Read OpenAI’s publisher and developer guidance
Now another visitor is emerging: the goal-oriented agent.
A crawler might ask, in effect:
What information exists on this page?
A retrieval system may need to answer:
Which product best satisfies this question?
An agent may be trying to accomplish:
Find the right product and take the next step for me.
Those are not variations of exactly the same machine behaviour.
They represent progressively deeper participation in the user journey.
Not every AI visitor is doing the same job
One reason the agentic web can become confusing is that “AI bot” is often used as a catch-all term.
The providers themselves make clearer distinctions.
Anthropic, for example, documents three separate robots: ClaudeBot for content that may contribute to model development, Claude-SearchBot for search, and Claude-User for web access initiated by a Claude user.See Anthropic’s crawler documentation
Perplexity similarly distinguishes PerplexityBot, which is intended to surface and link websites in its search results, from Perplexity-User, which can access pages in response to individual user requests.Read Perplexity’s crawler documentation
OpenAI also gives publishers separate controls around search discovery and potential training use.See OpenAI’s guidance for publishers
The practical implication is important.
Website teams increasingly need to think separately about:
Model-development access: Should this machine be allowed to collect content for potential model development?
Search and retrieval: Can the platform discover and retrieve the information when someone asks a relevant question?
User-directed access: Can an assistant fetch a page because an individual specifically asked it to?
Interaction: Can an agent understand and operate parts of the digital experience?
These are different requirements.
A binary question such as “Are AI bots allowed?” is beginning to lose useful meaning.
Readability is only the first layer
The first generation of AI-search optimisation has understandably focused on whether AI systems can access and understand website content.
That problem is real.
We explored it in AI Crawlability: Why a Fast Website Can Still Be Invisible to AI.
But the agentic web introduces a second requirement:
Actionability.
Imagine a SaaS website.
It is one thing for an AI system to understand:
A product demo is available.
It is another for an agent to understand:
- which control begins the demo-booking flow
- which inputs are required
- what each field means
- which time has been selected
- whether the form has successfully submitted
The first problem is about information.
The second is about interaction.
That distinction matters because agents do not consume a website exactly as a person does.
AI agents see more than the page you see
Google’s web.dev team now maintains specific guidance on building agent-friendly websites.
It explains that agents can interpret a website through three primary representations:
Screenshots provide visual context.
HTML and the DOM expose structure and relationships between elements.
The accessibility tree distils the interface into semantic information such as roles, names and states.Read Google’s guide to building agent-friendly websites
Modern agents can combine these representations.
That creates a useful connection between accessibility, front-end quality and agent experience.
Consider two controls that look identical to a human:
<div class="button">Book demo</div>and:
<button>Book demo</button>A person may understand both visually.
The second carries much clearer semantic meaning for software.
Google consequently recommends practices such as semantic HTML, correctly associated form labels, clearly reflected state changes and stable page layouts when building sites that agents can use.
These are not exotic “GEO hacks.”
They are good web practices becoming important to another audience.
In fact, Google makes the broader point directly: many practices that make a site more agent-friendly also make the site better structured and more accessible for humans.
Agent-friendly does not mean rewriting the website for AI
The arrival of a machine audience can easily produce the wrong response.
A brand hears that AI systems consume websites differently and starts creating special pages for every prompt, breaking content into tiny artificial sections or rebuilding everything around supposed LLM preferences.
Google’s current Search guidance explicitly pushes against that behaviour.
For generative AI search, Google says there is no requirement to break content into tiny chunks, no special schema required for AI visibility, and no need to rewrite content in a special AI-only style. It continues to recommend strong technical structure, crawlability and valuable people-first content.Read Google’s generative AI search myth-busting guidance
That is an important distinction.
The agentic web should not produce two completely disconnected internets: one for humans and another for machines.
The better objective is meaning that travels well.
Can a person understand the page?
Can a retrieval system access the facts?
Can an agent understand the interface?
Does the information remain the same across those experiences?
That is a much more durable goal than optimising for whichever supposed AI trick is fashionable this quarter.
The interface itself may become machine-addressable
Today, many agents interact with websites by interpreting interfaces designed for humans.
They effectively have to infer:
“That appears to be the search box.”
“This is probably the booking button.”
“That field probably expects an email address.”
A new set of technologies is exploring whether websites can make those capabilities explicit.
One example is WebMCP, currently being developed in the Chrome ecosystem.
WebMCP allows websites to expose structured tools to agents running in the browser. A website can define an action, explain what it does and specify the structured arguments it requires.Explore Chrome’s WebMCP documentation
Chrome’s documentation uses an example of a consultation-booking tool with defined inputs such as date, time, name and email.
Instead of an agent visually guessing its way through every step, the website can expose a clear contract for the action.
This is still emerging technology. WebMCP is currently available for experimentation through a Chrome origin trial, rather than being an established universal web standard.
But the direction is strategically significant.
For decades, websites have exposed interfaces primarily to humans.
They may increasingly expose capabilities to agents as well.
Agent experience introduces new questions for enterprise teams
This shift does not require enterprises to immediately create an “AI agent department.”
It does mean existing teams inherit new responsibilities.
Engineering and web teams
What representation does a machine visitor actually receive?
Can important information and controls be interpreted reliably?
SEO and digital teams
Which AI retrieval systems can access the site?
Where is the brand appearing, and which sources are influencing those answers?
UX and accessibility teams
Do controls clearly communicate their function?
Does the accessibility tree accurately represent the interface?
Are actions and state changes obvious?
Security teams
Which actions should agents be allowed to perform?
Where is authentication required?
Which decisions require explicit user confirmation?
Security becomes particularly important as agents move from reading to acting. Chrome’s WebMCP security guidance warns about risks such as indirect prompt injection, where malicious content attempts to influence an agent’s behaviour.Read Chrome’s WebMCP security guidance
Content and governance teams
Is the information the agent receives current and accurate?
Can different public sources present contradictory versions of the same fact?
Making information easier for machines to consume only helps when that information is also trustworthy.
The machine audience therefore turns out not to be solely an engineering problem.
It touches the entire digital operating model.
From user experience to agent experience
None of this reduces the importance of user experience.
Humans remain the audience businesses ultimately serve.
But there is increasingly a layer between the human and the website.
At Publive, we use Agent Experience Platform, or AXP, to describe technology and workflows built for that additional machine audience.
The concept is broader than traditional AI-search optimisation.
It asks:
Can agents access the information?
Can they interpret it reliably?
Can the brand be retrieved and represented accurately?
Can the machine-facing experience stay aligned with the original website?
And eventually, where appropriate, can software interact with the experience safely?
Different technologies address different parts of that problem.
For example,Publive AXP Edge focuses on the machine-delivery layer. Publive positions it as an edge-based approach that serves relevant AI crawlers a clean, structured representation of approved page content without requiring the human-facing website to be rebuilt.
WebMCP explores an interaction layer.
Accessibility and semantic web practices support another.
Content and claim governance address the integrity of what machines encounter.
The larger shift is therefore not “SEO for AI.”
It is the emergence of a second experience layer on the web.
The next web will be designed for humans and understood by machines
AI search asked brands a new question:
Can machines find and understand us?
The agentic web adds another:
Can machines reliably use what we have built?
The answer will matter differently by business.
A publisher may care primarily about retrieval and citation.
A SaaS company may care about comparison, qualification and lead generation.
An ecommerce business may eventually care about agents moving from product discovery into transactions.
A regulated organisation may care deeply about what information or actions software is allowed to access.
But the broader direction is the same.
The web is moving from a model where machines largely indexed human experiences to one where machines increasingly participate in those experiences.
That requires more than another optimisation tactic.
It requires websites whose content, structure and intent remain understandable regardless of whether the immediate visitor is a human, a search system or an agent acting for one.
The next generation of websites will still need to work beautifully for people. They will also need to make sense to the machines working on their behalf.
That is the agentic web.
Frequently Asked Questions
What is the agentic web?
The agentic web is an emerging model of the web where AI agents can discover information, interpret websites and increasingly complete tasks on behalf of users. It extends beyond AI search into software that can actively interact with digital experiences.
How is the agentic web different from AI search?
AI search primarily changes how information is retrieved and presented. The agentic web goes further by enabling AI agents to understand interfaces and potentially perform actions on behalf of users.
What is an agent-friendly website?
An agent-friendly website presents information and interactions in ways AI agents can reliably interpret. This can include semantic HTML, clear interface states, accessible forms, stable layouts and meaningful page structure.See Google’s agent-friendly website guidance
Does a website need separate content for AI agents?
Not necessarily. Google explicitly says sites do not need special AI-specific writing, forced content chunking or special schema to appear in its generative search experiences. The appropriate technical approach depends on the specific machine-access problem being solved.Read Google’s official guidance
What is the difference between an AI crawler and an AI agent?
A crawler generally retrieves information for purposes such as indexing, search or model development. An AI agent works toward a user goal and may retrieve information, interpret an interface and perform actions. Providers such as Anthropic and Perplexity already document separate user agents for different purposes.
What is WebMCP?
WebMCP is an emerging web technology that allows websites to expose structured tools to AI agents. These tools can describe actions and their required inputs, allowing agents to interact with websites more reliably. It is currently available for experimentation through a Chrome origin trial.Explore WebMCP
What is an Agent Experience Platform?
An Agent Experience Platform addresses how AI systems access, interpret and interact with a brand’s digital estate. Depending on the implementation, it can include machine-readable delivery, content workflows, AI visibility measurement, information governance and agent interaction.
