The web is getting a second reader. For thirty years we built pages for one audience: a human with eyes and a mouse. Now there is a second audience. AI agents visit sites to answer a question, compare options, or finish a task — for a person who will never see the page.
Most of the web serves that second reader badly
Fetch a normal page the way an agent does and you get 50–100 KB of markup — menus, banners, scripts — with the actual content buried inside. We spent twenty years making sites readable for one specific robot, the search crawler, and we called it SEO. Nobody has done the same work for agents yet. I think this is the most interesting gap on the web right now.
Two views of the same content, chosen at request time
My bet — and I built a product on it — is that the web splits into two views of the same content. The human gets the designed page. The agent gets clean Markdown, a discovery index, and structured data. The same truth, in the shape each reader can use. Not a separate “API version” that someone maintains by hand. The same content, served twice.llms.txt, content negotiation, and JSON-LD already exist
The pieces already have names. /llms.txt is becoming for agents what robots.txt was for crawlers. Content negotiation — an HTTP feature that has existed since the 90s — turns out to be exactly the right tool. JSON-LD carries the facts. None of this is new technology. What is missing is the habit of doing it on purpose, site by site.
You cannot improve what you do not measure
And the part almost everyone skips: “Agent-friendly” means nothing until it is a number. When I built Mokhai, the most useful design decision was turning agent-readiness into a 0–100 score across seven parts — discoverability, readability, schema, safety, health, conversion quality, discovery channels. The same decision made pages measurable at the byte level: a 50–100 KB HTML page becomes a 4–8 KB Markdown twin. The moment readiness became a number, site owners stopped debating and started fixing.