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, also to communicate with each other. The next web must also be understandable and usable by software agents acting on behalf of people and organisations.
Webpages vs Capabilities
The traditional web is organised around pages, links, forms and interfaces. Humans interpret those pages, make decisions and perform actions. AI agents work 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?
Three stages of Web readiness
What is an AI agent?
AI agent is software that can interpret a mission, obtain information, use tools or services and take actions within defined permissions. A conventional chatbot may answer a question, but an agent can go further: it may search, compare, calculate, request information from another system or perform an authorised action (according to a pre-defined plan of action, set by humans or systems).
The distinction matters because a website that is excellent for conversation between a chatbot and humans is not necessarily ready for autonomous or semi-autonomous interaction. Why?
AI agent is an artificial intelligence system that uses language models to acomplish a mission. Those language models can be Large Language Models (LLMs), Small Language Models (SMLs), or even MultiModal Language Models (MMLMs), which are capable of processing multiple types of data.
The architecture of AI agents (also called patterns, which can have different configurations) implements a reasoning framework, which allows complex tasks to be planned and executed, combining language processing, symbolic reasoning, interaction with the digital environment and strategic planning, which gives them a high degree of operational independence, like humans.
AI agent acts according to its circumstances and objectives, it is flexible within changing environments and goals, learns from experience and makes the best possible decisions given its perceptual and computational limitations (yes, everything has limits). To do this, an AI agent decomposes complex tasks into subtasks, which are executed in a planned way, thus creating a Chain-of-Thoughts, and each of those thoughts may be executed with different tools.
AI agents would not be able to implement automation effectively if they could only interact with services that do not require authentication. As a consequence, AI agents must be able to request and use human credentials (to access personal information in the cloud, such as an eMail box or any medical records) and technical credentials (to access VIP or corporate accounts).
Machine legibility
Machine legibility is the ability of software to discover, interpret and consistently use information published by a website. Useful signals can include:
Machine legibility does not automatically make a website agent-ready, but it is the foundation on which agentic interaction can be built.
Protocols change the Relationship
Agentic Web is not simply a collection of better HTML webpages. 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, and if different parts of a website provide conflicting information, the problem is greater than an appearent content inconsistency. As an example, please consider a product published with:
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.
KlaskerBot pinpoints inconsistencies between published representations of value: 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:
This is why Agentic AI readiness extends beyond SEO, AEO and structured data.
How does KlaskerBot sees the Agentic Web?
KlaskerBot 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.
Academy AITA
Academy AITA is your learning hub for Artificial Intelligence Trade Analysis, combining public research with practical evaluation of the digital environment.