Tools like ChatGPT, Perplexity, and Google AI Mode read your pages, weigh them against a handful of competitors, and hand back one synthesized answer to users. If your site isn't considered in that process, you miss out on potential visibility and brand awareness.
As more people use LLMs to complete tasks for them, like finding and buying products, missing that consideration starts to affect your bottom line.
This guide covers the difference between AI agents and search crawlers, and how to prepare your site for both. You'll learn which crawlers to allow or block, how to structure pages for extraction, what makes content trustworthy to an AI agent, and how to measure whether it's actually citing you.
How are AI agents different from search crawlers?
AI agents differ from search crawlers in what they do with a page once they've read it: a search engine crawler indexes it for future ranking, while an AI agent either indexes it for citation retrieval, absorbs it into model training, or fetches it once for a single live answer.
A crawler like Googlebot visits your site on a schedule and stores what it finds for later ranking across many future queries. An AI retrieval crawler like OAI-SearchBot also builds an index, but that index exists to supply citations for AI answers, not to rank pages in search results.
What are the key differences between AI agents, search crawlers, and user-triggered fetchers?
The key differences between AI agents, search crawlers, and user-triggered fetchers are what triggers them and what they do with your content.
Each type serves a different function:
- AI agents sweep or index content automatically, without a specific user request behind each visit. This category splits into three:
- Training crawlers (e.g., GPTBot, ClaudeBot, Bytespider) gather material to train AI models
- Retrieval crawlers (e.g., OAI-SearchBot, Claude-SearchBot, PerplexityBot) crawl content to build the index an AI system pulls from when answering a question
- User-triggered fetchers (e.g., ChatGPT-User, Claude-User, Perplexity-User) fetch a specific page only when a person asks the AI to read it — for example, when someone pastes your URL into ChatGPT.
- Search crawlers (e.g., Googlebot, Bingbot) crawl continuously on a schedule, index pages for ranking, and don't act on behalf of any one user
How do AI agents influence content discovery, comparison, and recommendations?
AI agents influence discovery, comparison, and recommendations by reading several sources, weighing them, and returning one synthesized answer. This process is also known as agentic search.
Agentic search largely removes the user from the process compared to Google search, where the user is the one that filters through different sources and chooses which one(s) they’d like to read.
But with AI search, you need to structure content for extraction, or risk staying invisible to both the AI and the person asking questions.
How do AI agents discover and access your site's content?
AI agents discover your site either through a scheduled crawl or a live fetch triggered by a user's question.
If a crawler can't reach or render a page, no amount of good writing on that page matters — the agent never sees it.
Which AI crawlers, bots, and user-triggered agents should I allow or monitor?
Allow AI crawlers, bots, and user-triggered agents that cite and mention content — blocking either removes your chance to be mentioned or cited in AI answers. Training crawlers are the one category worth blocking, if opting out of AI training is a priority for your site.
To opt out of training only, disallow training bots like GPTBot in your robots.txt file (a file that lists out rules for how bots should behave on your site), while leaving retrieval crawlers and user-triggered fetchers allowed. Blocking those instead cuts off your visibility in LLMs entirely.
What technical SEO elements help AI agents access and render your pages?
The technical elements that help AI agents access and render your pages are the same ones that help any crawler: clean HTML, fast page speed, and content that doesn't depend on JavaScript to appear.
Focus on:
- Put your real content in the raw HTML, not just in JavaScript. Most AI crawlers don't run JavaScript — they read whatever text is already in the page's first response and skip anything that only appears after a script runs.
- Make your pages load fast and reliably. AI crawlers abandon pages that time out — one analysis found that pages which frequently failed to load in time got cited about 18 times less often than pages that loaded reliably.
- Check your robots.txt file for AI-specific rules. A stale or default file can end up blocking or ignoring these crawlers without anyone realizing it.
Semrush's Site Audit flags rendering and crawlability issues on your existing pages — start there to confirm you're not blocking or hiding content by accident.
Once you configure Site Audit, you’ll get a report with your site’s health. Click the “Issues” tab and search “blocked” to surface any issues related to blocked crawlers.
Beyond blocked-crawler issues, fix every error, warning, and notice Site Audit flags — each one is a smaller barrier between your content and an AI agent.
How do site architecture, sitemaps, and internal links help AI agents discover important content?
Site architecture, sitemaps, and internal links help AI agents discover important content by organizing it into a demonstrable body of expertise instead of a scattered set of unrelated pages.
Platforms tend to favor domains that demonstrate comprehensive coverage of a subject over one isolated page — this is topical authority. So, the way your content is organized becomes part of your case for being a trustworthy source.
Build topical authority with:
- Pillar-and-cluster structure. Create one pillar page on a broad subject, then link it to and from focused subpages on its specific angles. The structure itself demonstrates coverage.
- Descriptive internal links. Connect related pages to each other with anchor text that names what's on the other end — this ties pages together as one body of expertise instead of disconnected content.
- No orphaned pages. A page with no links pointing to it doesn't contribute to your topical footprint, even if the content itself is strong.
- An updated XML sitemap. Keep one canonical, current list of your URLs.
What makes a site easier for AI agents to trust and verify?
A site becomes easier for AI agents to trust and verify when your business information matches everywhere it appears — not just on your own website — and when you have credible third-party mentions.
Before an AI agent repeats a claim about your business, it looks for other sources that back it up. A page that states your hours, address, or pricing correctly doesn't help if a directory listing or an old review platform contradicts it.
That means your business information needs to agree everywhere an agent might cross-check it, like business listings, review platforms, and social profiles.
Additionally, growing backlinks and unlinked mentions from reputable voices adds another layer of proof. Positive testimonials also give AI systems something concrete to weigh when vetting a source.
What markup or structured data helps AI agents understand my content?
The markup that helps AI agents understand your content is standard schema.org markup — Article, Organization, BreadcrumbList, and Product — implemented in JSON-LD, the same structured data Google already recommends for search.
Google’s own documentation on AI Overviews and AI Mode states plainly that no special schema.org markup is needed to appear in these features. Whether ChatGPT, Claude, or Perplexity read or weigh structured data when deciding what to cite isn’t something either company has confirmed, and third-party testing so far is inconclusive.
Even so, structured data is worth implementing regardless: Google’s search bot uses it for organic results, and it costs little to add once.
- Article: Identifies headline, author, and publish date for blog content
- Organization: Attaches your logo, name, and social profiles to your brand
- BreadcrumbList: Shows your site hierarchy in the search snippet instead of a raw URL
- Product: Surfaces price, availability, and review data on commerce pages
Implement these in JSON-LD, then validate with the Rich Results Test before publishing.
What credibility signals help AI systems verify your content?
Credibility signals that help AI systems verify your content include a named author, links to primary sources, and information that matches what other trusted sites say about you.
Here are some tips:
- Byline content with a real, credentialed person instead of "Staff Writer" or no name at all — an anonymous claim gives an agent nothing to corroborate
- Link to the original study, official documentation, or first-party data behind a claim, rather than restating what another blog already said secondhand
- Keep your business details identical across directories, review platforms, and social profiles — a mismatch with your own site undermines trust more than an omission would
- Attach reviews and testimonials to real, identifiable customers, not unattributed quotes with no way to check them
How do freshness, authorship, citations, and source transparency affect trust?
Freshness, authorship, citations, and source transparency affect trust because AI agents weigh how current and accountable a page is before repeating its claims.
A page with no update date and no named author reads as a lower-confidence source, even if the content itself is accurate.
Consider:
- Showing a visible "last updated" date on evergreen or fast-changing pages, and actually update the content when you change it
- Keeping author bios current and linked to their other published work, so the agent can see a pattern of expertise
- Citing your sources inline, with links, rather than asserting numbers with no attribution
How do I structure content so AI agents can extract and recommend it?
Structure content so AI agents can extract and recommend it by answering each question directly, in a self-contained chunk, using a format the agent can lift without rewriting.
Like in this below example from Semrush’s blog, where we answer the heading in the next passage:
Agents don't read your whole page the way a person might skim it — they pull the specific passage that answers the query and drop the rest.
How do I write section openings that AI agents can use as direct answers?
Write section openings AI agents can use as direct answers by starting each section with a complete, standalone sentence that mirrors the heading. If someone lifted just that first sentence out of context, it should still make sense and fully answer the question.
Avoid pronouns that point back to something outside the section ("this," "it," "they") — name the thing directly to avoid confusing AI agents (and readers).
Finally, keep the opening sentence to one idea. Save nuance for the sentences that follow.
What content formats do AI agents most reliably extract and cite?
The content formats AI agents most reliably extract and cite are tables, numbered lists, and bullet points — any format where each piece of information is complete on its own.
What matters is completeness, not the format itself. A single sentence, table row, or bullet just needs its own clear subject and claim, with nothing left unclear or pointing outside itself.
That said, some formats make this easier by default:
- Tables work well for anything with matching details across items — prices, features, specs — because each cell is already a clear fact, and the headers do the explaining a sentence would otherwise need to do
- Numbered lists work well for steps, because the order itself carries meaning a paragraph would have to spell out
- Bullet points work well for items that don't depend on each other — options, checklists, features — because each line stands as its own complete point
The same idea runs through the rest of this guide: content built in clear, complete pieces is easier for a person to skim and for an AI system to pull out and use.
How does content chunking affect whether an AI agent recommends a specific page?
Content chunking (structuring content into smaller, focused sections) affects whether an IA agent recommends a specific page because AI systems retrieve and rank individual passages, not full pages. A page with one strong section and five weak ones may only ever surface for the strong section.
If your best insight is buried inside a paragraph that also covers two other ideas, the retrieval system may not isolate it cleanly enough to use.
These tips help you create content chunks:
- Give each heading exactly one job — don't answer a second question under the same H2 or H3
- Keep paragraphs short enough that a single paragraph equals a single complete thought
- Avoid burying your strongest, most citable claim in the middle of a longer explanation; put it in its own sentence or bullet
- Use consistent terminology for the same concept throughout the page — chunking breaks when the same idea is described three different ways
How do I prepare pages for AI-driven decision workflows?
Prepare pages for AI-driven decision workflows by giving agents complete, structured facts instead of persuasive copy they have to interpret. When an agent is comparing you against competitors, it favors the source with the clearest, checkable data — not the one with the best-written pitch.
This isn’t limited to consumer purchases. 66% of B2B buyers regularly use AI to research products, vendors, or solutions for their job.
What information do AI agents need when comparing products, services, or vendors?
AI agents need specific, structured facts when comparing options — price, features, availability, and limitations — not general claims about quality.
Make sure each of these is stated explicitly:
- Exact pricing, including tiers, minimums, and what triggers a price change
- Named feature lists rather than vague benefit language ("fast" becomes "processes 10,000 rows per second")
- Stated limitations or exclusions — what the product doesn't do
- Who it's built for, in concrete terms (company size, industry, use case)
- Current availability, in stock status, or plan status
If this information only appears in marketing prose, an agent has to infer it, and inference introduces the risk of a wrong or outdated answer that it may still deliver confidently.
How should I structure pricing, comparison, alternative, use-case, and FAQ pages?
Structure pricing, comparison, alternative, use-case, and FAQ pages by leading with the exact facts an agent needs before any narrative explanation.
Here’s a simple framework to follow for each page:
- Pricing pages: State every tier, its price, and what's included, in a table. Avoid "contact us for pricing" if a real starting price exists.
- Comparison pages ("X vs Y"): Use consistent criteria across both sides — same rows, same order — rather than criteria that only appears for one option
- Alternatives pages: Name the competitor directly and support every trade-off claim with a verifiable fact or number
- Use-case pages: Open with who the page is for and what problem it solves in the first sentence
- FAQ pages: Phrase each question exactly as a user would ask it, and answer in one self-contained sentence an agent can lift without the surrounding page
How do I make product claims easier for AI agents to verify?
Make product claims easier for AI agents to verify by pairing every claim with a source, a number, or a way to check it.
For example, "industry-leading support" is not verifiable; "median first-response time of 12 minutes, per our Q2 support data" is.
Consider:
- Attaching a source or dataset to any performance claim
- Keeping pricing and feature claims on the page in sync with your actual product
- Updating pages when facts change — a stale price or discontinued feature weakens your credibility and can impact your visibility in LLMs and search
How do I find and fix gaps in my site's AI agent readiness?
Find gaps in AI agent readiness with Semrush’s Site Audit, which scores your site’s AI search health and flags the specific errors hurting it.
Site Audit scores your site’s AI search health, and you can click “# issues” to see every error affecting your AI readiness.
Click into any issue to see the affected elements, then fix each one to improve your odds of earning visibility in LLMs.
How do I identify which pages are already referenced in AI answers?
Identify which pages are already referenced in AI answers by checking the Cited Pages report in Semrush's AI Visibility Toolkit.
Enter your domain into Visibility Overview and review the "Cited Pages" metric alongside Mentions and Citations. This shows you exactly which URLs on your site are already being pulled into AI answers, not just which topics or prompts you show up for.
Once you know which pages are already earning citations, usePrompt Tracking to monitor whether that holds steady across the specific prompts that matter most to your business.
How do I find content gaps where AI agents recommend competitors but not me?
Find content gaps where AI agents recommend competitors but not you with a tool that analyzes prompts to find ones where you lack mentions.
Semrush’s Visibility Overview identifies prompts and topics that mention or cite your competitors, but not your site.
Enter your domain into the tool and click “Get started.” Scroll to “Topics & Sources” and click “Topic Opportunities” to see which topics mention your competitors but not you. Expand a topic to view particular prompts, or click the “Prompts” tab to view all prompts.
Prioritize gaps on commercial, decision-stage prompts first; those have the most direct business impact. Then, see if you can optimize existing content or create new content to address the gap.
Finally, use Prompt Tracking to confirm which content gap you’ve closed.
What technical issues stop AI agents from reading, trusting, or citing my site?
Technical issues stop AI agents from reading, trusting, or citing your site when they block access at the crawler, rendering, or speed level covered earlier in this guide.
Before moving on, confirm all three:
- Your robots.txt allows retrieval and fetcher bots, not just Googlebot
- Key pages render without JavaScript, or are pre-rendered for bots that don't execute it
- Pages load fast enough that they don't get skipped during a large crawl
Site Audit flags flags all three across your site in one place.
Fix the issues so AI agents can easily access and read your content.
How do I measure whether AI agents are choosing my site?
Measure whether AI agents are choosing your site by tracking citation share, brand mentions, and which specific pages get pulled into AI answers over time.
You can also track AI referral traffic, but treat it as a secondary signal. AI answer engines mainly influence whether a buyer hears about you before they ever land on your site, and that influence doesn't always show up as a session in your analytics.
Look for tools that track AI visibility directly: Semrush's Prompt Tracking for visibility on specific prompts, and Visibility Overview for overall mentions, citations, and cited pages.
What signals indicate AI agents are discovering and citing my content?
Signals like bot activity on your site and increased citations in LLMs indicate that AI agents are discovering and citing your content.
- Bot activity: Check your log files for AI crawler visits, or use Agent Analytics in Semrush Enterprise AIO to review the same data without digging through raw logs. Visits from AI bots confirm they can discover your content. No visits usually means your robots.txt is blocking a retrieval crawler or user-triggered fetcher — worth checking first.
- Increased citations: Review "Citations" and "Cited Pages" inside Semrush's Visibility Overview to see whether either number moves up as you work through this guide
How do I measure brand mentions, cited pages, and share of voice across AI answers?
Measure brand mentions, cited pages, and share of voice with specialized tracking tools.
Semrush’s AI Visibility Toolkit tracks mentions and cited pages. You can also review your share of voice compared to your top competitors across Google AI Mode, ChatGPT, Perplexity, and Gemini.
How do I compare my AI agent visibility against competitors over time?
Compare your AI agent visibility against competitors with a competitor gap analysis in Semrush's Competitor Research tool. Enter your domain and up to four competitors to get a report.
Review the "AI Visibility" chart to see how you compare to rivals, and check "Competitor Insights" for specific tips to close your gaps.
Open the AI Visibility Toolkit and see exactly where your competitors are outpacing you in AI answers — and where you can close the gap.
