r/Agent_SEO

I am new to this segment.i have LLMs.txt created but not sure if that's enough

Is there any good checker to say what's missing

Feels like covering a topic deeply matters, but not in the same way as before. Anyone seeing this shift?

Not in a “SEO checklist” way, more like how to make content easy to extract, summarize, and reuse.

been running into something on a few recent projects that I cannot find a clean answer to yet.

Googlebot and AI crawlers do not read pages the same way. and increasingly they seem to want different things from the same page.

Googlebot rewards internal linking depth, crawl structure, and the way authority flows across a domain. it wants pages connected to each other in ways that signal topical coverage and relevance. more pages, more depth, more interconnection generally helps.

AI crawlers seem to work differently. they parse what is directly on the page. document hierarchy. semantic structure. whether key information sits near the top of the DOM or is buried further down. whether content is rendered via javascript after initial load or sits in the raw HTML. they are forming their picture of your page from the document itself not from how it connects to everything else on your domain.

the conflict I keep hitting: a javascript rendered component that loads after interaction is often the right UX choice for a human visitor and sometimes the better SEO choice for engagement signals. but if that component contains your core product information an AI crawler may never see it at all.

same problem with tabbed content. great for human navigation on a product page. the hidden tabs either get missed entirely or read out of sequence by a parser that does not interact with the page the way a person does.

the position bias research compounds this further. the studies showing GPT favours first structural position, Claude pulls from middle, Gemini weights differently again means you genuinely cannot optimise page layout for all three simultaneously. you make a choice about which model matters most for your audience and accept that the others will read your page differently.

what I have been testing on recent builds is a layered approach. core product information in clean semantic HTML near the top of the document structure regardless of where it sits visually. javascript components for interactive elements that are not carrying critical information. a separate machine readable format alongside the human facing page for anything where AI visibility matters significantly.

the llms.txt standard is interesting here. cloudflare and hubspot have already implemented it. early data showing measurable AI traffic increases. but it is still very early and the adoption curve is unclear.

genuinely curious how others in this community are thinking about the two crawler problem practically. are you treating them as separate optimisation briefs or trying to find a single page architecture that serves both reasonably well.

I have been doing a deep dive into why Perplexity and ChatGPT choose specific sources over others, and it is honestly a bit frustrating. My site has solid authority and I use Ahrefs religiously to track my backlink profile and rankings, but I was still getting almost zero citations in the AI source bubbles. It felt like my content was being treated as noise. I started messing around with NetRanks to see if I could find a pattern in the sites that were getting cited. I ran a few of my competitor's pages through it and the difference was pretty immediate.

It turns out it isn’t just about the keywords or the domain authority. My content was written for human flow, which meant a lot of my facts were buried in conversational filler. The AI models weren't citing me because my factual density was too low for them to trust the attribution.

The competitor that was beating me had a much higher citation potential score on a sentence-by-sentence level. They weren't necessarily better writers; they were just using much more declarative, sticky phrasing that transformer models can easily parse as a source.

I have started using a hybrid workflow now where I do my standard research in Ahrefs, then run my final drafts through NetRanks to "harden" the specific sentences I want the AI to pull. It’s a weird shift to realize we’re now optimizing for the confidence threshold of a model rather than just a search crawler, but I’ve finally started seeing my own source bubbles pop up in the answers.

Are any of you auditing your content at the sentence level yet, or is everyone still just focusing on the high level SEO stuff?

Suggest me some of the best claude Co-work SEO related videos...Where all the things are Describe properly related to SEO and Claude and alll the essential things

Curious how other SEOs are handling multi-location sites right now. Specifically running into issues with similar location pages getting crawled but inconsistently indexed, and occasional cannibalization between them

Google's March core update was extremely volatile, and brands that produce original research gained 22%.

Early analysis shows that brands with original research - proprietary surveys, unique frameworks, first-person benchmarks - got a huge boost in the latest update. In organic results and in AI Mode citations.

Content that synthesised existing thinking without adding anything new lost ground.

This makes sense if you think about what both AI and Humans need from your content in 2026.

An LLM can synthesise and summarise any topic on demand, tailored to any user, in seconds. That's literally what it does. So content that does the same thing - repackages what's already known - is, well, pointless.

What the model can't do is originate. It can't run a survey. It can't publish proprietary benchmarks. It can't develop an entirely new framework or concept.

In short, it can't produce Information Gain. Net new knowledge.

This update data suggests Google is now actively weighting that distinction.

We have a simple framework against which we measure every piece of content we produce.

Level 0: No information gain.
Level 1: Interpretive Gain - a new slant on existing knowledge.
Level 2: Empirical Gain - new data or original research.
Level 3: Conceptual Gain - a genuinely new framework or mental model.

How are you looking at this?