What Is Agentic Search? (And Why SEOs Need to Pay Attention)

AI search exists on a spectrum.

At one end: a human asks an AI a question and gets a fast, generated response.

At the other: an AI receives a goal and browses the web on a human’s behalf. It evaluates your brand, makes a decision, and leaves no trace in your analytics.

That’s agentic search.

And it’s already emerging.

ChatGPT’s deep research, Gemini’s agentic mode, and Perplexity’s research features are early expressions of it. Shopping within ChatGPT and booking tables without ever visiting a website are where it’s heading.

AI systems are already running multi-step evaluations with less human direction at each step.

The brands that show up in those evaluations aren’t waiting to see how this develops.

They’re optimizing for it now.

By the end of this guide, you’ll know what agentic search is, how it differs from typical AI search, and how you can prepare your brand for it.

What Agentic Search Actually Is

Agentic search is AI that searches and acts on your behalf — not just composing an answer from its training data, but going out to find information, use tools, and complete tasks.

At the simpler end of the agentic search spectrum, the AI retrieves sources and synthesizes a response.

At the more complex end, the AI agent receives a search goal, breaks it into sub-tasks, searches across multiple sources, cross-references what it finds, and takes action, without waiting for your input at each stage.

Examples of Agentic Search in Action

At the simpler end of the agentic search spectrum, you give an AI tool a prompt like “Which project management software is best for a remote team of ten?”

It won’t just produce an answer from its training data. It’ll go online, search for comparison articles, pull pricing and feature information from review platforms, and synthesize a recommendation.

Move further along the AI search spectrum and the behavior gets more complex.

For instance, imagine you ask the AI to research the competitive landscape in your market. It formulates a plan, then runs multiple searches across different source types — news coverage, review platforms, company pages, industry analysis.

It cross-references what it finds, and you get a structured report.

You’re still the one taking action based on this report, but this is a step up from the fairly simple, synthesized response we’re now used to.

Further still: some agents don’t need a prompt at all. Configured with a recurring search task, like monitoring competitor pricing, flagging new entrants, or summarizing industry news weekly, they run on a schedule.

And at the furthest end of the agentic search spectrum, the AI finds the right option, evaluates it against alternatives, and completes a transaction on your behalf. You asked for a recommendation. It booked the table.

Both OpenAI and Google have published open protocols specifically designed to make this possible (more on them soon).

Why This Is Different from What SEOs Already Know

Agentic search challenges some of the core assumptions SEO has operated on for years.

Here are the three that matter most.

Rankings Matter Less Than Before for Overall Visibility

AI tools are built to pull from a deliberately diverse range of sources, not just the highest-ranking pages.

A single search query triggers retrieval across multiple source types: editorial content, review platforms, community forums, company pages. No single ranking position dominates that process.

AI tools also heavily weigh up content and brand relevance when forming responses, versus factors like website authority, which is more important for SEO.

That doesn’t mean backlinks don’t matter — they do. But topical depth and relevance to the searcher’s intent are the focus in these tools.

Finally, when an AI tool processes a search, it generates multiple related sub-queries, pulling from the results of each. This is called query fan-out.

Your ranking for the original keyword is just one input into a much wider retrieval process. This makes broader topical coverage a key component of AI search in general. This is how you show AI agents that you’re worth citing, recommending, and taking action on.

Your Content Depth Is Now a Competitive Advantage

As Crystal Carter, Head of AI Search & SEO at Wix, puts it: “LLMs don’t get tired of reading 45 pages about your business.”

The average user won’t read countless pages of product documentation. But an agent will — and it’ll use what it finds to make a recommendation.

FAQs, knowledge base articles, documentation, case studies — content that might rarely surface in a standard browsing session becomes evidence in an agentic evaluation.

Crystal gives Levi’s sustainability documentation as an example.

A human visitor might not find it. If you were wondering if Levi’s were sustainable, you’d probably look them up on a single trusted site.

Compare that with what Perplexity AI does to answer the question “Are Levi’s sustainable?”

It conducts a deep dive into Levi’s site.

It evaluates evidence from 15 different sources.

It reads multiple pages from Levi’s own site, including their sustainability report, details on the sustainability of their fibers, their stance on human rights, and a page on slavery from a domain in a separate geography (Levi’s UK).

To succeed in agentic search, you need to make sure agents can answer any questions about your brand your users may have.

Breadth Now Matters Just as Much as Depth

AI systems don’t simply retrieve results. They actively research, compare, and filter brands before a human ever sees a recommendation.

Your brand isn’t being ranked once. It’s being audited across sources.

If we take the Levi’s example again, ChatGPT doesn’t just look at Levi’s own content to answer the sustainability question.

It also looks at official rating bodies, third-party research, and media publications. It acts more like a professional researcher than a human conducting a low-stakes product search question.

An agentic system evaluates brands through layered filters like:

• Can it find you clearly?
• Does it understand you correctly?
• Are you validated elsewhere?
• Does it trust you enough to recommend you?

If you fail any of those layers, you can disappear entirely from the final answer.

Your Site Needs to Be Usable By Agents, Not Just People

Increasingly, AI agents interact with businesses through structured agentic protocols designed for machine-to-machine communication.

Instead of just relying on what’s in a page’s HTML, AI agents are moving toward standardized protocols, like the Agentic Commerce Protocol (ACP) and Natural Language Web (NLWeb).

This changes what “being accessible” actually means.

Content that only exists inside a visual interface — FAQs that expand on click, pricing tables rendered dynamically, product comparisons loaded via JavaScript — may never exist in the structured layer agents rely on to retrieve and execute actions.

And if they can’t access it, they can’t use it.

That matters because AI agents are increasingly the ones deciding what to include in their recommendations and what to ignore. The human only sees your site if you’re in those recommendations.

So the question is no longer just: “Can people find my website?”

It’s: “Can AI systems clearly understand and use my business information without friction?”

Because in this new system, if your business isn’t easy for AI to access and act on, you may not show up at all.

What Agents Actually Look At

An agent evaluating your brand might find everything it needs on a single page of your website.

But when it does go looking further, it’s not just gathering information. It’s also checking whether the rest of its sources agree.

An agent corroborates, actively checking whether the picture is consistent across everything it finds.

Here are some of the key places agents look:

Your Website

Agents are likely to prioritize sites that are easy to parse and extract from. They look for:

Clear, up-to-date pricing in plain HTML (not hidden behind interactions).

Feature descriptions that explain capabilities — not just marketing claims.

Positioning that makes it obvious who the product is for (and who it isn’t).

Review Platforms (G2, Capterra, Trustpilot)

Agents read review content for specificity, covering things like use case, company size, outcomes, and integrations.

Community Signals (Reddit & Other Forums)

Agents look at user sentiment on community platforms to cross-check vendor claims.

A brand that talks about itself one way and gets discussed differently in communities creates a consistency gap that leaves agents hesitant to recommend your brand (at least without caveats).

Third-Party Editorial

Agents also look at comparison articles, analyst coverage, and industry endorsements.

Appearing consistently in credible “best X for Y” content is a positive signal.

6 Things to Do Before Agentic Search Goes Mainstream

Agentic search isn’t fully mainstream yet, but the infrastructure is being built now.

The brands that will be well-positioned are the ones that start taking action before their rivals are even aware of what agentic search is.

Here’s how to make sure you’re one of those brands.

1. Run a Cross-Source Consistency Audit

Check your pricing, features, and positioning across your own site, your G2 and Capterra profiles (or any other platforms your target audience users), and comparison articles where your brand appears.

Flag and correct every contradiction.

Make this a recurring part of your workflow. Old positioning language lingers in third-party content long after you’ve updated your own pages.

2. Build Hub Pages for Your Highest-Value Queries

If you don’t have them already, create new standalone pages that fully answer the key questions: what you do, who it’s for, how it compares to other solutions, what it costs, and what customers say.

3. Pressure-Test Your Declared Audience

Pull up your homepage, pricing page, and top comparison content.

Ask: can an agent clearly extract who this is for, what problem it solves, and what makes it right for a specific profile?

To make this concrete, paste the content into an AI tool and use this prompt:

If the output is vague or generic, your positioning is too.

4. Ask Customers for More Detailed Reviews

Most reviews are vague: “Great product, really helpful team.”

That doesn’t help AI systems understand when your product is actually a good fit.

Instead, ask customers to be more specific about how they use it and what changed.

For example, in your review requests, you can say:

“If you’re happy to leave a review, it would be really helpful if you could include:

• What you use the product for
• Your company size or team type
• The problem you were trying to solve
• The outcome or result you saw
• Any tools you integrate with”

5. Check Your Accessibility

Make sure your pricing, FAQs, and feature comparisons are in plain HTML.

Also check your forms and CTAs. If an agent needs to book, enquire, or transact on a user’s behalf, it needs to be able to find and use the form. So don’t hide them behind JavaScript.

6. Implement Agentic Search Protocols

While agentic search protocols are still new and being actively developed, understanding how they work and implementing them on your site can help you prepare for wider rollouts.

For more information on which protocols matter and what they do, read our guide to agentic search protocols.

7. Monitor Your AI Footprint

Right now, here are two things you can actually track to monitor your AI footprint:

Run Regular Brand Queries

Open ChatGPT, Perplexity, and Google AI Mode, and search for your brand by name.

Then search for the category queries a buyer would use — “best [product type] for [your target audience].”

In both cases, document what comes back. Is your brand mentioned? Is what’s being said accurate? Is it consistent with your current positioning?

Do this monthly and track how things change over time.

If your positioning is wrong or outdated, update your homepage, pricing, and comparison pages first (these are usually the sources AI systems rely on most).

If competitors are being favoured, strengthen your comparison content and aim to get more third-party reviews.

If you’re missing entirely, check whether your key pages are crawlable, indexable, and clearly describe your use case.

Check Your Server Logs for AI Crawler Activity

Your server logs record the bots that visit your site, including AI crawlers.

The ones to track include:

• GPTBot: OpenAI’s training crawler
• OAI-SearchBot: Powers real-time ChatGPT search results
• ClaudeBot: Anthropic’s crawler
• PerplexityBot: Perplexity’s crawler
• Google-Extended: Google’s AI training crawler

Look for:

• How often they’re visiting
• Which pages they’re accessing
• Whether those pages return clean 200 responses (successful loads)

If key pages are returning errors (404s), that’s a signal those pages may not be properly accessible to AI systems.

This won’t tell you whether an agent recommended you or ruled you out.

But it’s one of the earliest available signals of how AI systems are interacting with your site.

Get Your Site Ready for Agentic Search

Agentic search is already here. And as time goes on, complex agentic tasks — like signing up for a tool or buying on behalf of the user — will only become more common. That’s why it’s worth preparing for full agentic search right now.

Start by figuring out where you stand currently.

Tools like Semrush’s AI Visibility Toolkit show you how AI systems currently perceive your brand across platforms. That’s your baseline before you tackle anything else. Learn how to use it in our Semrush AI visibility guide.