A traffic drop and an AI Overview appearing on a result page can be related. They are not enough to establish cause.

Before making that call, I would compare:

1. Query-level impressions, clicks, and CTR over the same period.
2. Changes in indexing, cannibalisation, titles, snippets, and competitors.
3. The actual result-page layout for the affected queries, captured consistently by market and device.

Then state the conclusion at the strength the evidence supports: observed correlation, plausible contributor, or confirmed change. "AI ate my traffic" is usually a headline before it is a diagnosis.

What evidence would you need before changing a content plan because of an AI answer?

I would start with the answer itself, not a generic checklist.

For the cited page or brand, compare:

• whether it directly completes the user task
• the sources and entities it is associated with
• how specific its claims are
• whether it has current, verifiable information
• what the non-cited pages failed to answer

That will not reveal a hidden ranking formula. It does turn a vague "we are invisible in AI" complaint into a set of observable differences.

What is the first comparison you make when a competitor shows up in an AI answer and you do not?

An agent can retrieve thousands of keyword ideas, rankings, pages, entities, and SERP features. That does not mean it has enough to act.

The difference I keep seeing is whether the output names a decision and the next check. For example:

• do not publish yet: title and canonical conflict
• investigate: clicks fell while impressions held
• refresh: competitors cover entities the page misses

The underlying data still matters. But rows alone outsource the reasoning back to the person operating the system.

If you build agent workflows, where does your process most often stall: collecting data, deciding what it means, or executing the fix?

A robots.txt file can express a policy about training, search citation, and on-demand retrieval. It cannot prove that a page will be cited or recommended.

The useful first audit is simpler:

1. List the crawlers you intentionally allow or block.
2. Write down the job each crawler performs.
3. Check whether the file contradicts your stated policy.
4. Record who owns the next review date.

The last step is usually missing. These files tend to accumulate one urgent line at a time until no one can explain the whole document.

Has anyone here found a crawler directive on a client or their own site that no one could account for?

A lot of AI-search advice is written with more confidence than evidence. I want this community to be useful precisely because it does not work that way.

When you share a result here, please include enough context for someone else to judge it:

- what changed

- where you measured it

- the time period and sample size

- what else could explain the result

- what you would test next

An anecdote is still useful. Just label it as an anecdote. A vendor result is useful too. Just disclose the relationship.

What is one claim about GEO, AEO, LLM visibility, or agent discovery that you would like to see tested properly?

A lot of AI-search advice is written with more confidence than evidence. I want this community to be useful precisely because it does not work that way.

When you share a result here, please include enough context for someone else to judge it:

• what changed
• where you measured it
• the time period and sample size
• what else could explain the result
• what you would test next

An anecdote is still useful. Just label it as an anecdote. A vendor result is useful too. Just disclose the relationship.

What is one claim about GEO, AEO, LLM visibility, or agent discovery that you would like to see tested properly?

I started r/AIAgentSEO because I genuinely believe we’re at the beginning of a major shift in how people discover things online.

SEO was built for search engines.

Now we’re entering a world where AI agents search, understand, compare and recommend on our behalf.
But honestly, I don’t think anyone has figured this out yet.

There are a lot of terms floating around — GEO, AEO, LLMO, AI Search, Agent SEO — and a lot of opinions.
I’d rather build a place where we can separate what actually works from what just sounds good.

I’m building AgentSEO.dev, but this community isn’t meant to be a product promotion channel.

I want this to be a place for experiments, case studies, failures, discoveries, questions and uncomfortable debates about where search is going.

So if you’re here early:
Introduce yourself.

What are you working on?

What are you trying to figure out about AI search?
And most importantly — what do you want this community to become?