Imagine a world where your brand’s website is never visited — only called, or invoked, by AI. No homepage, no scrolls, no clicks.
A user’s AI books the consult, pulls the quote, and compares your plan to competitors — without the user ever opening a browser. Your brand appears only because a machine understands what you do and how to call it.
That world is the agentic web — the next evolution of the semantic web. Where the semantic web made information machine-readable, the agentic web makes it actionable. In the agentic web, AI-powered agents don’t just look up information; they perform tasks. They coordinate services, make recommendations, and execute actions on behalf of users.
That is where your Brand AI Concierge lives: a machine-readable service layer that exposes what your brand can execute so agents can act on your behalf. Brands that build this layer will exist in the agentic web. Brands that don’t will not be called.
The Shift to Agents
The digital economy was built on the assumption that humans would navigate the web. We optimized for clicks, pageviews, and funnels. That assumption is being replaced by a new reality: machines, not humans, now navigate information.
Tasks once performed manually — such as research, comparison, and execution — are now being handled by agents acting on behalf of users: “Book the consult.” “Find a vendor.” “Start the claim.” “Get a quote.” No search box. No landing page.
Agents don’t browse or read. They evaluate only one thing: what they can act on, with what inputs, and at what level of trust. This isn’t a future scenario; it’s already taking shape. Those who participate early gain a compounding advantage.
The Problem with Today’s Digital Footprint
Most organizations believe they are “digitally ready” because they have websites, content, APIs, and documentation. But every one of these assets assumes a human audience. Pages are written for readers, APIs for developers, and navigation for human understanding. None of this is agent-ready by default.
Agents, whether AI models or digital assistants, do not interpret meaning the way people do. They can’t read content, navigate interfaces, or infer intent from design. They rely on clear, machine-readable signals — like structured data — that define what actions are possible, the rules that apply, and how to trigger them.
Without that data, your brand is not simply deprioritized; it becomes invisible in the agentic ecosystem.
This is the critical disconnect: brands continue to publish human-readable information while agents require machine-readable definitions of capabilities — the actions they can perform and how to execute them. Because agents default to the first callable option, the cost of being late is not lower visibility; it is exclusion.
What We Call an Agentic Entry Point
An Agentic Entry Point is the machine-facing equivalent of your brand’s homepage. It isn’t designed for humans to read, but for agents to understand what your brand can do, what information they need to perform a task, and how to trigger it safely.
An Agentic Entry Point has three defining traits:
1. It exposes tasks, not pages.
Instead of a button that says “Book a demo,” it publishes a defined “BookDemo” action that tells the agent what the task is, what inputs are required, and what rules apply.
2. It defines clear rules of engagement.
This includes what data is needed, what the expected response looks like, how authentication works, and how errors are handled — all in a format that a machine can interpret directly.
3. It is discoverable by agents without human context.
It’s not buried in developer docs or implied through your website’s interface. It’s published in a structured, machine-readable format that agents can find and understand without human help.
In the agentic era, this is the new battleground for visibility. The brands that are callable are the ones that will be found.
Schema.org: The Bridge Between Humans and Machines
The agentic web is not starting from scratch. The industry has already adopted a common language for expressing entities, services, and even actions: Schema.org. What began as a way to help search engines extract meaning from pages is becoming the dual-purpose contract that serves both humans and machines.
Structured markup already provides what agents need most:
- Named entities such as products, services, offers, organizations, and people
- Semantic meaning for consistent definitions across publishers
- Action vocabularies such as BookAction, ScheduleAction, and QuoteAction
Today, LLMs connect to systems through OpenAPI and Model Context Protocol (MCP). These standards define how to call a function, not what it means.
Without defined semantics, both MCP and OpenAPI act as blind execution layers. Their usefulness depends on the semantic groundwork already established — the kind provided by Schema.org structured data.
Schema.org is already the foundation of the agentic web — we just haven’t used it yet as the execution layer that lets agents understand and act on brand capabilities.
Structured Content: The Foundation of Agentic Action
Agents cannot act on what they do not understand. Before an agent can book, compare, or recommend, it must first understand what the thing is, how it relates, and where it fits in context. That understanding comes from structured content, not from agent logic itself.
Whether implemented as JSON-LD on pages or as a managed content knowledge graph, this layer provides:
- Entity resolution: identifying that a service is the same offering across multiple URLs
- Context: showing how a service belongs to a brand and serves an audience
- Reusability: enabling structured definitions to power search, LLMs, and agents
The agentic layer doesn’t replace your existing content — it builds on top of it. If your content isn’t structured, agents have nothing reliable to act on.
All the work organizations have done with schema markup, entity modelling, and content knowledge graphs wasn’t wasted effort. It was the foundation for what comes next.
The Dual-Mode Web: Serving Humans and Machines
The arrival of agents does not make the human web obsolete. People will continue to read, compare, and explore visually. But a website that only serves humans is now incomplete — the same information must also be consumable by machines without interpretation.
Think of it as dual-delivery publishing:
| Old Web Requirement | New Agentic Web Requirement |
|---|---|
| Pages must explain to humans |
Data must explain itself to machines
|
| Links connect documents | Schema Markup connects entities and actions |
| CTAs trigger UI flows | Agentic Entry Points trigger tasks |
- Human-consumable: UX, narrative, persuasion
- Machine-consumable: structure, semantics, invocation
Why SEO Teams Are Uniquely Positioned to Lead This Transition
If anyone in the organization has already been preparing for the agentic era, it is SEOs — even if they didn’t call it that. The core skills required for the next wave are the same skills SEOs have been forced to develop ahead of everyone else:
- Structuring meaning for machines (Schema.org)
- Modelling entities, not just pages
- Governing published semantics for accuracy
- Designing for machine interpretation, not just humans
The only thing that changes is the goal state. Where SEO optimized for visibility in search, Agentic Entry Points optimize for eligibility in machine-driven execution — and Generative Engine Optimization (GEO) optimizes for LLM surfaces where there may not be a results page at all.
The work is not replaced; it is promoted. SEO evolves from influencing rankings to influencing outcomes in generative and agentic environments. This is not asking SEOs to learn something foreign — it is asking them to own the logical next layer of the thing they have already been doing.
How Product Leaders Define Agentic Entry Points
The shift becomes real when someone inside your company decides what agents should be able to do with your brand. That decision starts with product strategy, not engineering.
A Practical Roadmap for Product Leaders
1. Identify 3–5 high-value repeatable tasks where agent completion would create revenue or reduce friction.
Examples: BookConsultation, RequestQuote, MatchPlan, CheckEligibility, StartReturn.’
2. Rewrite those as explicit machine intents, not human goals.
Not “Help customers schedule time” — but a defined ScheduleAction with named inputs and return structure.
3. Declare the engagement contract before implementation.
Inputs, pre-conditions, authentication requirements, success/failure semantics — written as if a machine will read them (because it will).
4. Use Schema.org Action vocabulary as the publishing layer for interoperability.
5. Start small. Publish one Agentic Entry Point before scaling.
Momentum in this channel is earned by existence, not completeness. Being callable once is more valuable than planning to be callable broadly.
6. Decide the governance boundary before code is written.
Who approves new intents? Who maintains the Schema Markup? What must never be invoked autonomously? The cheapest time to decide is before exposure.
This is not a technical migration. It is an operational evolution from “pages that inform humans” to “contracts that empower machines.”
The Strategic Advantage of Being Early to The Agentic Web
Early adopters gain three compounding advantages:
- New-channel visibility: You become callable before competitors. Agents default to trusted, invokable options, and habits form early.
- Defensive positioning: Once agents complete your category’s tasks through a competitor, you lose eligibility, not just ranking.
- Organizational clarity: Defining tasks and rules for machine invocation forces alignment and surfaces ambiguity hidden in content, processes, or UI flows.
Becoming agent-ready is not only a marketing advantage; it is operational discipline. Whether a brand chooses to participate or not, agents will handle more digital interactions every year. The only variable leaders control is when their brand becomes machine-callable, not if it will.
Doing this work now is not a gamble — it is a timing advantage. The same modelling, structuring, and publishing will be required later, but under greater pressure and with less control. Acting early is simply a smarter version of the same inevitability.
Early movers define the standards that agents learn first. In this new ecosystem, being first is not just an advantage — it is the foundation of discoverability.
You do not need a new platform, a full taxonomy of actions, or a governing body to begin. You need one clear decision: Which single task should an agent be able to complete with your brand, without a human in the loop?
- Define it.
- Express it using Schema.org Action vocabulary.
- Publish it as your first Agentic Entry Point.
That single act shifts your brand from watching the change to shaping it. The web will continue to serve people, and the agentic layer will increasingly serve on their behalf. The brands that are callable first will stay ahead the longest.
Mark van Berkel is the Chief Technology Officer and Co-founder of Schema App. A veteran in semantic technologies, Mark has a Master of Engineering – Industrial Information Engineering from the University of Toronto, where he helped build a semantic technology application for SAP Research Labs. Today, he dedicates his time to developing products and solutions that allow enterprise teams to leverage Schema Markup to boost their AI strategy and drive results.