1. Human-readable
Information is presented primarily for people. Navigation, visual layout, forms and conventional interfaces are the main interaction mechanisms.
FOUNDATIONS
The Agentic Web
The web was built primarily for people to read and interact with. The next web must also be understandable and usable by software agents acting on behalf of people and organisations.
From pages to capabilities
The traditional web is organised around pages, links, forms and interfaces. Humans interpret those pages, make decisions and perform actions.
An AI agent works differently. An agent may need to discover a product, understand its properties, compare alternatives, check availability, calculate delivery, follow a return policy and eventually complete an authorised transaction.
This changes the fundamental question from:
Can a person use this website?
to:
Can an authorised agent understand and use this website
reliably?
Three stages of web readiness
What is an AI agent?
An AI agent is software that can interpret a goal, obtain information, use tools or services and take actions within defined permissions.
A conventional chatbot may answer a question. An agent can go further: it may search, compare, calculate, request information from another system or perform an authorised action.
The distinction matters because a website that is excellent for conversation is not necessarily ready for autonomous or semi-autonomous interaction.
Machine legibility
Machine legibility is the ability of software to discover, interpret and consistently use information published by a website.
Useful signals can include:
- JSON-LD and Schema.org structured data
- Product, Offer, price and availability information
- APIs and structured feeds
- robots.txt and XML sitemaps
- security.txt
- llms.txt and related machine-readable resources
- .well-known resources
- clear canonical URLs
- consistent policies and terms
- documented machine-facing capabilities
Machine legibility does not automatically make a website agent-ready. It is the foundation on which agentic interaction can be built.
Protocols change the relationship
The Agentic Web is not simply a collection of better HTML pages. New protocols are being developed to allow agents, services, businesses and payment systems to communicate using defined structures and trust mechanisms.
Klasker Academy examines these layers separately because protocol support is one of the measurable foundations of Agentic AI readiness.
Static Readiness is not Agentic Readiness
A website can publish excellent metadata and still be difficult for an agent to use.
These are related but different measurements. A website may therefore have strong Static Readiness while still requiring substantial work before it becomes genuinely agent-ready.
Truth matters
Agents make decisions from information. If different parts of a website provide conflicting information, the problem is greater than an ordinary content inconsistency.
Consider a product published with:
- JSON-LD price: €49.99
- Visible page price: €59.99
- Structured availability: InStock
- Actual store status: Out of stock
A human may notice the discrepancy. An automated agent may interpret one of these representations as authoritative and act on incorrect information.
Agent readiness therefore requires not only
machine-readable information, but trustworthy and
consistent information.
Klasker calls significant inconsistencies between published representations Promise Drift.
Trust and authorisation
An agent should not be trusted simply because it claims to be an AI agent.
Agentic systems introduce questions about identity, authorisation, intent, transaction limits and accountability.
Depending on the task, a trustworthy interaction may require mechanisms such as:
- verified agent identity
- cryptographically protected intent
- explicit user mandates
- spending limits
- transaction policies
- refund and cancellation rules
- human override
- auditable transaction records
This is why Agentic AI readiness extends beyond SEO, AEO and structured data.
How Klasker sees the Agentic Web
Klasker approaches Agentic AI readiness as a layered system:
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The foundations lead to the technologies and measurements that make the Agentic Web possible.