How to Optimize Your Website for AI Search (What Google Actually Says)

We just audited our own website against Google's AI Optimization guide. We went through all 52 requirements, scored ourselves against each one, and came out the other side with a 76% pass rate and one big takeaway: there is no AI SEO.

Not "AI SEO is different." There is literally no separate discipline. Google says so explicitly. Everything the guide recommends is traditional search best practice, applied well. If you have been paying consultants to "optimize for AI Overviews," this post might save you some money.

What the Google AIO Guide Actually Says

We expected new technical requirements -- special tags, AI-specific sitemaps, maybe formatting instructions for language models. Instead, the guide says repeatedly that good SEO is AI optimization.

Here is what the guide does NOT recommend:

• No llms.txt file. Google does not use llms.txt. It is a community proposal that some other services support, but Google's crawlers ignore it. If you spent time creating one, it is not hurting anything, but it is not helping your AI search visibility either.
• No special AI markup. There is no <ai-summary> tag, no data-ai-answer attribute, no hidden metadata format that feeds AI Overviews. These do not exist.
• No content chunking for AI. You do not need to restructure your pages into "AI-digestible" chunks. Google's models understand full pages. Breaking your content into disconnected fragments actually hurts coherence.
• No rewriting content for AI consumption. Google explicitly discourages creating separate content streams for AI. One piece of good content serves both human readers and AI systems. Writing "in a way AI can understand" is a solution to a problem that does not exist.
• Schema.org is not required for AI features. Structured data helps with rich results (those enhanced search listings with stars, prices, and images). It does not determine whether your content appears in AI Overviews. If you have been adding schema markup purely for AI visibility, the ROI is not there.

What Actually Matters: The 5 Things

After reading the full guide and mapping it against our own site, five factors stand out as genuinely important for AI search visibility.

1. Content Quality: Say Something Nobody Else Can

Google's AI systems pull from content that demonstrates first-hand expertise and unique perspectives. This is E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) applied to AI selection.

AI Overviews synthesize from multiple sources. If your page says the same thing as 50 others, the AI has no reason to cite you. What gets cited is original data, a concrete case study, or a perspective grounded in actual experience.

We write about running a company with AI agents because we actually do it. Not as a thought experiment. We have agents handling code review, security audits, content production, and customer communication right now. That first-hand experience is what makes content worth citing.

2. Crawlability: If Google Cannot Reach It, AI Cannot Use It

Every page you want in AI search results must be indexed, accessible, and returning 200. The audit turned up issues we had missed: pages behind client-side rendering that Googlebot could not fully execute, redirect chains, and orphaned pages with no internal links.

Our blog has 296 posts. After the audit, we verified every one is linked from the blog index and returns 200. We fixed 12 broken internal links in the process.

3. Page Experience: Responsive, Fast, Semantic HTML

Core Web Vitals, mobile responsiveness, and clean semantic markup still matter. Google's AI systems parse page structure to understand content hierarchy. If your H2 tags do not reflect logical structure, or your page takes 8 seconds to load, you are disadvantaged in both traditional and AI search.

Use <article>, <section>, <nav>, <header>, <footer> correctly. Use heading levels that reflect actual hierarchy. These elements help both search engines and AI systems understand what your page is about.

4. FAQ Content: The Highest-Signal Format for AI Overviews

This was the most actionable finding. Pages that directly answer common questions in a clear question-and-answer format have the strongest correlation with AI Overview appearances. Not because of FAQ schema markup (remember, schema is not required for AI features), but because the content format itself -- a clear question followed by a direct answer -- maps naturally to how AI Overviews work.

We already had a comprehensive FAQ page, but the audit revealed we were missing FAQ sections on our core product pages. We added 23 Q&As across 3 pages, each answering real questions we get from prospects and customers.

5. Preparing for Agentic Browsing

This is the genuinely new part of the guide, and it is forward-looking. Google describes a future where AI agents browse the web on behalf of users -- filling out forms, comparing products, completing transactions. Preparing for this means:

• Stable page layouts that do not shift unpredictably as elements load
• Semantic HTML so agents can identify interactive elements (buttons, forms, navigation)
• Accessible interactive elements with proper ARIA labels and logical tab order
• Predictable URLs that an agent can construct programmatically

If you are building secure, authenticated agent interactions, this matters doubly. Your API surfaces and documentation pages need to be agent-navigable, not just human-readable. We think about this constantly because our platform is built for AI agents that interact with web services, APIs, and infrastructure on behalf of organizations.

What We Changed on agent.ceo

Theory is cheap. Here is what we actually did after the audit:

• Added FAQ sections to 3 core pages with 23 total Q&As, sourced from real customer questions
• Added a clear product definition in the first 100 words of the homepage. Before the audit, visitors had to scroll past a hero animation and two feature blocks before learning what agent.ceo actually does. Now the first paragraph tells you.
• Added founder bio for E-E-A-T. Google's AI systems weigh author credibility. We added a visible founder profile with relevant credentials and links to prior work.
• Expanded schema markup. Not for AI Overviews (remember, it does not affect those), but because we were already benefiting from Article and BreadcrumbList JSON-LD on all 296 blog posts. We added HowTo schema to tutorial posts and Organization schema to the homepage to improve rich result eligibility.
• Verified all 296 blog posts are crawlable and indexed. Every post linked from the blog index, every link returning 200, zero orphaned pages.

Total score: 76% on first pass against the 52-requirement checklist. The remaining 24% are either not applicable (no e-commerce pages) or in progress.

The Myths We Stopped Worrying About

After the audit, we actively stopped investing time in several things the SEO industry was telling us to do:

llms.txt files. Google does not use them. We had one. We did not delete it (it does no harm), but we stopped maintaining it.

Content chunking for AI. We had started restructuring some long-form posts into shorter, "AI-friendly" sections. We reversed this. Google understands full-length content. Coherent, thorough articles outperform fragmented ones.

Special AI markup. We had evaluated adding custom meta tags for AI crawlers. Unnecessary. Standard HTML meta tags, Open Graph, and semantic markup are sufficient.

Rewriting content for AI. Some posts had been drafted with a "write for the AI reader" approach -- shorter sentences, simpler vocabulary, more repetition of keywords. We reverted to writing for humans. Google's guide explicitly says this: write for people, and AI systems will understand it.

The biggest time savings came from stopping these activities, not from adding new ones. AI search optimization is subtractive. Stop doing unnecessary things. Focus on content quality, crawlability, and semantic structure.

What This Means for Your Site

If you are evaluating whether your content strategy works for AI search, the framework is simpler than the industry wants you to believe. Three questions:

1. Does your content say something only you can say? (If not, AI has no reason to cite you over the other 50 results.)
2. Can Google actually crawl and index every important page? (Check. Do not assume.)
3. Is your HTML semantic and your page experience solid? (Run Lighthouse. Fix what it flags.)

That is the checklist. There is no secret fourth step involving prompt engineering your meta descriptions or training a custom model on your sitemap. Evaluating whether AI is doing the job requires honest measurement, and the same applies to evaluating your AI search readiness. Measure against the actual Google guide, not against what someone is selling you.

If you want to see this implemented at scale -- 296 blog posts, semantic HTML, FAQ optimization, structured data, and enterprise-grade security -- take a look at what we are building at agent.ceo. We run a company where AI agents handle real operational work, and our website is both the product and the proof.