What does it mean to be agent ready?

For years, businesses have optimised their websites for search engines like Google. We researched keywords, wrote blogs, and hoped to rank at the top of the results page.

But search is fundamentally changing. Instead of humans browsing links, AI assistants and software agents (such as ChatGPT, Gemini, and Claude) are searching the web on behalf of users. They do the research, compare options, and might even make purchases or book flights autonomously.

To survive in this new landscape, businesses need to adapt to Agentic SEO. It is no longer just about being visible to humans; it is about being agent ready.

What Does it Mean to Be Agent Ready?

Being agent-ready means your digital presence is built so that autonomous AI systems can easily find your information, trust it, and interact with your website.

Instead of just optimising text for a human reader, you are preparing your site for machine usability.

This shift changes the core question of SEO from “Do we rank for this keyword?” to “Can an AI agent find our data and confidently complete a task on our site?”

The Two Pillars of Agentic SEO

To understand how to prepare your business, it helps to look at agent readiness through two distinct layers: the discovery layer and the execution layer.

1. The Discovery Layer (Can the AI find and trust you?)

Before an AI agent can recommend your product, it needs to read and verify your data.

• Machine-Readable Content: AI agents do not care about beautiful web designs. They read raw data. Sites need clear formatting, server-side rendering (so content is visible in the initial code), and structured data (like Schema.org) that explicitly tells the AI what a price, size, or opening hour is.
• The llms.txt File: Much like a traditional sitemap.xml guides Google’s crawlers, a new standard called an llms.txt file is emerging. This is a simple, plain-text file at the root of a website that serves as a summary and reading list specifically for AI models.
• Depth and Authority: AI tools use strict quality filters to avoid “hallucinating” (making things up). They look for high-quality, data-driven content with clear expert insights. Vague, superficial marketing fluff will simply be ignored.

2. The Execution Layer (Can the AI act on your site?)

The future of search goes beyond answering questions. Users are starting to tell AI agents to “book the cheapest flight to Madrid next Tuesday” or “order a replacement filter for my specific coffee machine.”

• Frictionless Paths: If your website relies heavily on pop-ups, login walls, or complex visual menus that require hovering, an AI agent will get stuck and abandon the task.
• Exposing Capabilities via APIs: To be truly agent-ready, websites are beginning to share their functions through clean APIs (Application Programming Interfaces).

Traditional SEO vs. Agentic SEO

The transition to an agentic web changes how marketing teams operate daily.

Traditional SEO	Agentic SEO
Focuses on human impressions and clicks.	Focuses on machine legibility and task execution.
Done periodically via manual keyword audits.	Operates continuously using AI agents to track intent.
Optimises for visibility on a results page.	Optimises for inclusion in AI summaries and direct actions.
Relies heavily on visual layouts and page design.	Relies on structured text data, APIs, and clean code.

Official Google Documentation and AI Standards

Google is already actively releasing documentation, developer tools, and web standards to help webmasters prepare for this shift. Here is how Google defines and manages the agentic web:

1. The “Google-Agent” User Agent

Historically, Googlebot crawled the web to index pages for search. Now, Google has introduced a brand new user agent specifically called Google-Agent.

According to Google’s official developer guides, this is a user-triggered fetcher. It does not crawl the web automatically. Instead, it is triggered when a human user asks an AI assistant (like Google’s Project Mariner) to perform a direct action. When the assistant goes to navigate a website, click buttons, or complete forms on behalf of the user, it identifies itself as Google-Agent.

2. WebMCP (Web Model Context Protocol)

To stop AI agents from guessing how to use websites by scanning screenshots or messy HTML, Google (along with Microsoft) has proposed an open web standard called WebMCP.

WebMCP allows developers to directly expose structured “tools” to in-browser AI assistants. Instead of scraping a page, an agent can ask the browser for a list of available tools. Your website can then natively tell the agent: “I have a tool called ‘search_flights’ that requires a date and a destination.” This makes AI transactions faster, uses fewer data tokens, and achieves nearly perfect task accuracy.

3. Google-Extended Control

If you want to control how Google uses your content, Google provides a control token called Google-Extended. By adding this to your robots.txt file, you can tell Google whether or not your content can be used to train Gemini and other AI models.

The clever part of this system is that blocking Google-Extended does not remove your website from Google’s traditional search results, giving publishers granular control over their data in the AI era.