AI Agents And Infrastructure

The first observation that LePage made was that AI agents will use the same web infrastructure as search engines. The main point he makes is that the data the agents are using comes from the regular classic search indexes.

“Agents will use the same infrastructure the web already has.

• Search to discover relevant entities.

• ‘Domain authority’ and trust signals to evaluate sources.

• Links to traverse between entities.

• Content to understand what each entity offers.

I find it interesting how much money is flowing into AIO and GEO startups when the underlying way agents retrieve information is by using existing search indexes. ChatGPT uses Bing. Anthropic uses Brave. Google uses Google. The mechanics of the web don’t change. What changes is who’s doing the traversing.”

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AI SEO = Longtail Optimization

LePage also said that schema‑structured data, semantic density, and interlinking between pages are essential for optimizing for AI agents. Notably, he pointed out that the AI optimization work being done by AIO and GEO companies is essentially basic long‑tail query optimization.

“AI intermediaries doing synthesis need structured, accessible content. Clear schemas, semantic density, good interlinking. This is the challenge most publishers are grappling with now. In fact there’s a bit of FUD in this industry. Billions of dollars flowing into AIO and GEO when much of what AI optimization really is is simply long‑tail keyword search optimization.”

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What Optimized Content Looks Like For AI Agents

LePage, who is involved in AI within the WordPress ecosystem, said that content should be organized in an “intentional” manner for agent consumption—structured markdown, semantic markup, and content that’s easy to understand.

“Presentations of content that prioritize what matters most. Rankings that signal which information is authoritative versus supplementary. Representations that progressively disclose detail, giving agents the summary first with clear paths to depth. All of this still static, not conversational, not dynamic, but shaped with agent traversal in mind.

Think of it as the difference between a pile of documents and a well‑organized briefing. Both contain the same information. One is far more useful to someone trying to quickly understand what you offer.”

Later in the article he offers a seemingly contradictory prediction: in an agentic AI future, content may no longer need a traditional website, just a “pile of documents.” Nevertheless, he recommends that content retain structure so that information is well‑organized at both the page and site levels, with clear hierarchical relationships and interlinking.

He adds that future websites will host AI agents that communicate with external AI agents, leading to a paradigm where content is split from the website and displayed in ways that make sense for users, separate from today’s concept of visiting a website.

“Think of this as a progression. What exists now is essentially Perplexity‑style web search with more steps: gather content, generate synthesis, present to user. The user still makes decisions and takes actions. Near‑term, users delegate specific tasks with explicit specifications, and agents can take actions like purchases or bookings within bounded authority. Further out, agents operate more autonomously based on standing guidelines, becoming something closer to economic actors in their own right.

The progression is toward more autonomy, but that doesn’t mean humans disappear from the loop. It means the loop gets wider. Instead of approving every action, users set guidelines and review outcomes.

…Before full site delegates exist, there’s a middle ground that matters right now.

The content an agent has access to can be presented in a way that makes sense for how agents work today. Currently, that means structured markdown, clean semantic markup, content that’s easy to parse and understand. But even within static content, there’s room to be intentional about how information is organized for agent consumption.”

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