Technical SEO for generative search: Optimizing for AI agents

Technical SEO extends beyond indexing to how content is discovered and used, especially as AI systems generate answers instead of listing pages.

For generative engine optimization (GEO), the underlying tools and frameworks remain largely the same, but how you implement them determines whether your content gets surfaced — or overlooked.

That means focusing on how AI agents access your site, how content is structured for extraction, and how reliably it can be interpreted and reused in generated responses.

Agentic access control: Managing the bot frontier

From a technical standpoint, robots.txt is a tool you already use in your SEO arsenal. You need to add the right crawlers within your files to allow specific bots their own rights. 

For example, you may want a training model like GPTBot to have access to your /public/ folder, but not your /private/ folder, and would need to do something like this:

User-agent: GPTBot
Allow: /public/
Disallow: /private/

You’ll also need to decide between model training and real-time search and citations. You might consider disallowing GPTBot and allowing OAI-SearchBot.

Within your robots.txt, you also need to consider Perplexity and Claude standards, which are tied to these bots:

Claude

• ClaudeBot (Training)
• Claude-User (Retrieval/Search)
• Claude-SearchBot

Perplexity 

• PerplexityBot (Crawler)
• Perplexity-User (Searcher)

Adding to your agentic access is another new protocol — llms.txt, a markdown-based standard that provides a structured way for AI agents to access and understand your content.

While it’s not integrated into every agent’s algorithm or design, it’s a protocol worth paying attention to. For example, Perplexity offers an llms.txt that you can follow here. You’ll come across two flavors of llms.txt:

• llms.txt: A concise map of links.
• llms-full.txt: An aggregate of text content that makes it so that agents don’t have to crawl your entire site.

Even if Google and other AI tools aren’t reading llms.txt, it’s worth adapting for future use. You can read John Mueller’s reply about it below:

GEO focuses more on chunks of information, or fragments, to provide precise answers. Bloat is a problem with extractability, which means AI retrieval has issues with:

• JavaScript execution.
• Keyword-optimized content rather than entity-optimized content.
• Weak content structures that fail to provide clear, concise answers.

You want your core content visible to users, bots, and agents. Achieving this goal is easier when you use semantic HTML, such as:

The goal? Separate core facts from boilerplate content so your site shows up in answer blocks. Keep your context window lean so AI agents can read your pages without truncation. Creating content fragments will feed both search engines and agentic bots.

Dig deeper: How to chunk content and when it’s worth it

Structured data: The knowledge graph connective tissue

Schema.org has been a go-to for rich snippets, but it’s also evolving into a way to connect your entities online. What do I mean by this? In 2026, you can (and should) consider making these schemas a priority:

• Organization and sameAs: A way to link your site to verified entities about you, such as Wikipedia, LinkedIn, or Crunchbase.
• FAQPage and HowTo: Sections of low-hanging fruit in your content, such as your FAQs or how-to content.
• SignificantLink: A directive that tells agents, “Hey, this is an authoritative pillar of information.”

Connecting information and data for agents makes it easier for your site or business to be presented on these platforms. Once you have the basics down, you can then focus on performance and freshness.

Performance and freshness: The latency of truth

AI is constantly scouring the internet to maintain a fresh dataset. If the information goes stale, the platform becomes less valuable to users, which is why retrieval-augmented generation (RAG) must become a focal point for you.

RAG allows AI models, like ChatGPT, to inject external context into a response through a prompt at runtime. You want your site to be part of an AI’s live search, which means following the recommendations from the previous sections. Additionally, focus on factors such as page speed, server response time, and errors.

In addition to RAG, add “last updated” signals for your content. is one way to achieve this, along with schema headers, which are critical components for:

• News queries.
• Technical queries.

You can now start measuring your success through audits to see how your efforts are translating into real results for your clients.

Dig deeper: How to keep your content fresh in the age of AI

Measuring success: The GEO technical audit

You have everything in place and ready to go, but without audits, there’s no way to benchmark your success. A few audit areas to focus on are:

• Citation share: Rankings still exist, but it’s time to focus on mentions as well. You can do this manually, but for larger sites you’ll want to use tools like Semrush.
• Log file analysis: Are agents hitting your site? If so, which agents are where? You can do this through log analysis and even use AI to help parse all of the data for you.
• The zero-click referral: Custom tracking parameters can help you identify traffic origins and “read more” links, but they only paint part of the picture. You also need to be aware that agents may append your parameters, which can impact your true referral figures.

Measuring success shows you the validity of your efforts and ensures you have KPIs you can share with clients or management.

Scaling GEO into 2027

Preparing your GEO strategy for 2027 requires changes in how you approach technical SEO, but it still builds on your current efforts. You’ll want to automate as much as you can, especially in a world with millions of custom GPTs.

Manual optimization? Ditch it for something that scales without requiring endless man-hours.

Technical SEO was long the core of ranking a site and ensuring you provided search bots and crawlers with an asset that was easy to crawl and index.

Now? It’s shifting.

Your site must become the de facto source of truth for the world’s models, and this is only possible by using the tools at your disposal.

Start with your robots.txt and work your way up to structure, fragmented data, and extractability. Audit your success over time and keep tweaking your efforts until you see positive results. Then, scale with automation.