As generative AI and autonomous AI agents reshape how people find, consume, and act on information online, SEO isn’t going away — it’s evolving. James LePage, Director of Engineering for AI at Automattic and co-lead of the WordPress AI Team, recently explained how traditional SEO fundamentals apply in the context of AI agents and what publishers should focus on to remain discoverable in an agent-powered web.
This article breaks down his insights, translates them into actionable guidance, and provides a forward-looking view of how to optimize content for both today’s search engines and the emerging landscape of AI-mediated discovery.
Understanding AI Agents and the Web’s Core Infrastructure
One of the first clarifications LePage offers is that AI agents rely on the same web infrastructure that search engines have always used to discover, evaluate, and traverse content. These agents don’t replace the fundamentals of the web; they navigate it differently.
Specifically:
- Agents use search indexes to find relevant entities.
- They rely on domain authority and trust signals to gauge source credibility.
- Links remain the pathways connecting entities.
- Content provides context and understanding of what each entity offers.
In other words, AI doesn’t invent a new internet. It traverses the existing ecosystem — search infrastructure, links, signals, and content — in a new way.
This insight helps cut through much of the hype that suggests AI SEO is some exotic new discipline. Instead, it highlights the continuity between classic search optimization and the emerging needs of AI agents.
AI SEO Is Essentially Long-Tail Optimization
LePage draws a strategic connection between what many companies call “AI optimization” and traditional SEO practice. He observes that what AI optimization tools advocate — structured data, semantic richness, and interlinked content — is not fundamentally different from long-tail keyword optimization that seasoned SEO practitioners have been doing for years.
Why this matters:
- AI agents synthesize answers across many related queries, not just isolated keywords.
- Long-tail optimization looks at how a range of related phrases and concepts connect, which naturally aligns with agent needs.
- Structured markup and intentional content design help agents parse and assemble content into meaningful answers.
LePage stresses that AI optimization isn’t about gaming models or writing for machines; it’s about providing clear, semantically dense content that agents can interpret and trust.
What Content Looks Like to AI Agents
If you think about AI agents as specialized readers with a mission to extract and synthesize information, then the structure and clarity of your content become crucial.
LePage defines effective content for AI agents as:
- Clearly organized: Information should have logical structure, with summaries and progressively deeper detail.
- Semantically marked up: Using proper HTML hierarchy and schema enhances machine understanding.
- Linked purposefully: Internal links should reflect content relationships and topic clusters.
He compares this to the difference between:
“a pile of documents and a well-organized briefing.”
Both contain the same data, but only one lets an agent quickly grasp relevance, authority, and scope.
This analogy is useful because it highlights a shift in emphasis: AI agents don’t just crawl text, they interpret context and relationships. When content is intentionally structured — not just loosely written — it becomes far more “agent-friendly.”
How Intentional Organization Works at Scale
LePage emphasizes the importance of structuring content in ways that reflect human organizing principles, not machine heuristics. This involves:
1. Hierarchical Structure
Use clear heading hierarchies (H1, H2, H3) and semantic HTML so that core ideas, subtopics, and supporting details are easy for both humans and machines to follow.
2. Semantic Richness
Semantic markup and schema tell machines what your content represents — e.g., an FAQ, tutorial, review, or product page — and how pieces relate to one another. This enables agents to map user intent to specific content elements.
3. Internal Linking
Well-designed internal linking strengthens the relationships between related content, clarifying topical authority and helping agents understand entire subject areas, not just isolated topics.
4. Purposeful Presentation
Deliver what matters most upfront. Agents prioritize concise, accurate summaries before diving into details, so introductory overviews and clear sectioning improve both agent and user comprehension.
Why Traditional Websites Still Matter
Even as the web evolves toward agent-driven interactions, LePage argues that current AI agents still depend on static content and existing web infrastructure. There’s speculation about future use cases where AI agents communicate directly, for instance, through APIs or database connections, but today’s reality still centers on web content that crawlers and agents can access just like search engines do.
He outlines a potential evolution where:
- AI agents may operate more autonomously.
- Websites could become data sources rather than destinations.
- Agents embedded on sites may exchange structured information with external agents.
However, this future is gradual. In the near term, content creators should focus on clarity, structure, and semantic richness rather than chasing early visions of fully decoupled web content.
How Publishers Should Prepare Today
To be visible and useful in an AI-mediated web — whether for traditional search or AI agent discovery — content must reflect intent and structure that agents can parse reliably.
Focus on Semantic Signals
Accurate use of structured data (schema) and semantic HTML allows machines to interpret:
- What your page is
- What entities and concepts does it contain?
- How it relates to other content
This is especially important given that AI agents synthesize answers across sources. Sectioned, well-labeled content becomes easier for systems to integrate into answers.
Enhance Internal Content Relationships
Clustering related topics and linking them coherently builds topical depth. Agents use link graphs to understand context and authority, just as traditional crawlers do.
Prioritize Clarity Over Cleverness
Clever or overly creative writing can make content harder for agents to interpret. Write for clear communication first, using a hierarchical structure and explicit relationships.
How This Relates to Traditional SEO Practices
What may sound like a new discipline — “SEO for AI” — is in many ways a continuation and extension of established SEO principles:
- Long-tail query optimization remains valuable because agents synthesize answers to nuanced questions.
- Structured data and internal links were already best practices; they’re now crucial for agent comprehension.
- Authority signals like backlinks still matter because agents use domain trust to rate content credibility.
LePage’s perspective suggests that rather than inventing wholly new workflows, publishers should elevate and scale existing best practices with an eye toward structured clarity and semantic meaning.
What the Agent-Ready SEO Workflow Looks Like
Here’s a practical breakdown of how to prepare content for both search engines and AI agents:
Step 1: Topic Definition
Start with a clear understanding of:
- User intent
- Related subtopics
- Contextual questions
Step 2: Structured Content Assembly
Use a logical hierarchy of headings and sections to organize information. Include summaries and deeper detail blocks so agents can extract large chunks of meaning easily.
Step 3: Semantic Markup
Apply schema (e.g., Article, FAQ, HowTo) and rich HTML semantics so machines interpret each section correctly.
Step 4: Interlinking and Clustering
Connect related content with purposeful internal links to build topical authority and contextual relevance.
Step 5: Continuous Refinement
Monitor content performance and refine structural and semantic elements based on analytics and AI interactions.
This workflow aligns SEO with the emerging demands of AI agents, without abandoning core SEO fundamentals.
FAQs: SEO for AI Agents
Q1: Do AI agents use a different search infrastructure than search engines?
No. AI agents still rely on the same web infrastructure — search indexes, domain authority signals, links, and content — that search engines have always used for discovery and evaluation.
Q2: Is “SEO for AI agents” a new discipline?
Not fundamentally. It builds on traditional SEO — long-tail optimization, structured data, and internal linking — adapted to how AI agents interpret and traverse content.
Q3: What makes content “agent-friendly”?
Agent-friendly content is well-structured, semantically rich, and clearly organized. It uses logical heading hierarchies and a schema so machines can parse the meaning effectively.
Q4: Will websites disappear in an agentic future?
LePage suggests a gradual evolution where content may be consumed via agents without traditional browsing. However, static web content still matters today, and websites remain necessary for structured, authoritative content.
Q5: Should content strategies focus only on AI agents?
No. Effective content strategies address both human readability and machine interpretability, maintaining clarity for readers while providing structure for agents.
Conclusion: The Bridge Between SEO and AI Agents
James LePage’s insights provide both reassurance and direction. They reassure us that AI agents aren’t overthrowing the web’s foundations; they’re navigating them differently. And they direct publishers to focus on structure, semantics, and clarity — the building blocks of effective SEO for humans and machines alike.
Rather than a radical new process, “SEO for AI agents” is an evolution of existing best practices — rewritten with an emphasis on making content easy to traverse, easy to interpret, and easy to integrate into synthesized answers. By organizing content like a well-structured briefing, optimizing semantic signals, and reinforcing topical authority across pages, publishers can prepare for both traditional search success and the growing influence of AI agents in how information is discovered, understood, and used.
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