Marie Haynes started using "agentic search" in late 2024 to describe a shift that changes what SEO measures and what it rewards. Users stop typing queries into a search box and start handing outcomes to AI agents. The agents visit your pages on the user's behalf, read, compare, and answer. The human never clicks. This guide covers what that means, how it differs from other AI search terms, and a working list of what publishers can do about it.
Key takeaways
- Who coined it — Marie Haynes popularised the term in an SEO context starting late 2024, framing it as the biggest mindset shift in SEO history.
- Two meanings — Agent-orchestrated retrieval (a technical pattern) and agent-mediated web use (the SEO meaning). This guide covers the second.
- Why it matters — AI agents increasingly arrive at your pages instead of humans. They choose which sources to cite, and that choice moves traffic and brand visibility.
- What to do — Write canonical answer blocks, ship comprehensive Schema.org markup, publish an llms.txt, expose content server-side, and let known AI user agents through.
Where the term comes from
"Agentic" as a technical adjective predates the SEO conversation by years. In AI engineering, an agentic system is one that plans, uses tools, and iterates toward a goal rather than answering a single prompt. The word entered SEO language through Marie Haynes, who in early 2025 published "The Agentic Web Is Here: 5 AI Shifts I'm Discussing with Clients" and began framing the next wave of AI impact on search under this label.
Haynes' thesis is direct: Google publicly acknowledged that Search is becoming agentic at the end of 2024. Consumer AI products like ChatGPT, Gemini, and Perplexity had already started shipping browsing agents that use search engines internally. The centre of gravity in information retrieval is moving from a person reading a SERP to an agent synthesising an answer from several pages it visited for you.
The implication for publishers is practical, not philosophical: optimise for the agent as reader, because the agent is the one deciding what the user hears.
The two meanings you will encounter
Because "agentic" is the current favourite adjective in AI, the SERP for agentic search mixes two very different things. Keep them separate.
Meaning 1: Agent-orchestrated retrieval (technical)
A pattern inside AI systems where a language model plans a multi-step retrieval, issues queries against a vector database, filters results, and composes an answer. OpenSearch, Elastic, Azure AI Search, and AWS OpenSearch all ship features under this label. Relevant to engineers building internal knowledge agents. Not relevant to web publishers.
Meaning 2: Agent-mediated web use (SEO)
A workflow where a user gives an AI product an outcome, and the product visits websites, reads them, reasons over them, and returns a synthesised answer. ChatGPT Agent, Operator, Perplexity Pro Search, Gemini Deep Research, and Google's agentic Search experiments all live here. This is the meaning Haynes uses. The rest of this guide covers only this one.
How agentic search works from both sides
From the user's side, a single prompt describes an outcome: "find me the three best noise-cancelling headphones under 300 euros with ten-hour battery" or "summarise the arguments for and against GLP-1 use in athletes." No query syntax, no refinement, no scanning.
From the agent's side, a sequence runs under the hood:
- Decompose the request into sub-tasks and disambiguations.
- Issue queries to one or more search engines, typically Google or Bing, sometimes multiple in parallel.
- Fetch pages from the top results, usually five to twenty, depending on the agent.
- Extract specific data points, summaries, or structured records from each fetched page.
- Synthesise a single response with citations back to the source pages.
The agent decides which pages are worth reading, which to cite, and which to ignore. It does not click ads. It rarely engages with newsletter pop-ups or cookie walls. Many agents will silently skip pages that require login, render entirely in JavaScript without an initial HTML payload, or return an interstitial before content.
Your page still matters. The reader is just different.
Agentic search vs generative search, GEO, AEO, and LLM SEO
These terms are often used interchangeably and they should not be. Each targets a different surface and a different model of how content reaches the user.
| Feature | Agentic search | Generative search | GEO | AEO | LLM SEO |
|---|---|---|---|---|---|
| What it is | |||||
| Core unit of interaction | Task outcome given to an AI agent | Query; answer synthesised in-page | Query; focus on generative answer sources | Query; focus on direct answers and PAA | Content surfaced inside LLM chats |
| Who consumes your page | An autonomous agent | A search engine model | A search engine model | A search engine ranker | An LLM during retrieval |
| Primary surface | Agent product (ChatGPT Agent, etc.) | SERP with AI Overview or Copilot | SERP with generated answer | SERP with snippet/PAA | LLM chat window |
| Optimisation priorities | |||||
| Structured data matters | High: read raw JSON-LD | High | High | High for snippets | Medium to high |
| llms.txt relevant | Yes (emerging) | Not yet standard | Not yet standard | Not applicable | Yes (emerging) |
| Brand co-occurrence weight | Very high | High | High | Moderate | High |
| Click loss risk | Highest: agent answers in-place | High | High | Moderate | High |
| Measurement | |||||
| Shows in GA4 referrals | Some agents send referrer | Yes | Yes | Yes | Some; many do not |
| Citation tracking tools | Profound, Peec, Otterly | AI Overview trackers | Perplexity trackers | Standard SEO tools | LLM visibility tools |
Short version:
- Generative search is a search engine returning a synthesised answer instead of links (Google AI Overviews, Bing Copilot Search).
- Agentic search is an AI product autonomously using the web, with search as one tool among several.
- GEO is a discipline for influencing what generative answers say about your topic.
- AEO is an older label for optimising toward direct answers, featured snippets, and PAA.
- LLM SEO is the umbrella term for making your content appear in LLM outputs at all.
If you optimise well for agentic search, you usually cover most of GEO, AEO, and LLM SEO along the way. The inverse is not true.
A longer treatment lives in GEO vs AEO vs LLM SEO vs Agentic Search.
What changes for SEO when users stop clicking
The mechanics of ranking do not disappear. The distribution of traffic and the signals that matter shift. Expect these six changes to be visible in your analytics and in your citation trackers over the next twelve to twenty-four months.
- Click-through rates on informational queries fall, because agents answer in place. Long-tail "how to" and "what is" traffic drops first.
- Brand mentions in trusted co-occurrence contexts start to matter more than backlinks. Agents read who is mentioned alongside what, and use that to judge authority.
- On-page clarity becomes a citation signal. Ambiguous pages get skipped; pages with a clear answer block get cited.
- Machine-readability becomes a soft ranking factor for citations. Schema.org, tables, clean headings, and well-structured lists are easier to extract.
- Freshness signals get weighted more. Agents often prefer recent content; exposed
dateModifiedand visible revision notes help. - Barriers quietly remove you from answer sets. Cookie walls, JS-only rendering, anti-bot gates, and registration walls turn into silent exclusions.
How to optimise for agentic search
Seven tactics, in order of increasing effort. The first three should be on every page in your content inventory within a quarter.
- Write a canonical answer block near the top of each page. Two to four sentences that directly answer the primary query, using the exact language a user would phrase it in. Agents often extract this verbatim. Treat it as the machine-readable abstract.
- Publish a comprehensive
llms.txtat your root. List your highest-value pages, with short descriptions, grouped by topic. The spec is young, but the common agents already read it. CTAIO Labs ran a thirty-day citation experiment on this exact intervention — methodology and the per-engine delta in llms.txt — 30-Day Citation Experiment. - Expand Schema.org coverage. At minimum:
Article,FAQPage,Product,HowTo, andOrganization. Include a realauthorwith a profile URL,dateModified, and where relevantreviewedBy. Agents read raw JSON-LD even when Google does not surface rich results. - Make entity links explicit. When you mention a person, company, product, or concept, link it to its canonical reference (Wikipedia, Wikidata, official domain). Agents follow these to disambiguate entities and to build trust.
- Ship content server-side. Many agents run JavaScript but many fall back to the initial HTML. Put the answer, headings, and structured data in the first response, not after hydration.
- Open the door to known AI user agents. Allow
GPTBot,OAI-SearchBot,PerplexityBot,ClaudeBot, andGoogle-Extendedinrobots.txtunless you have a specific reason to block them. Blocking training crawlers does not stop inference-time crawlers; review each one separately. - Publish primary evidence. Agents disproportionately cite pages with original data, first-hand testing, interviews, or clearly labelled sources. Secondary summaries of someone else's work rarely get cited once a first-hand alternative exists in the index.
The agentic search engines to watch in 2026
Not every AI with a search button is agentic, and not every agentic product is material for your traffic. The ones worth monitoring this year:
- ChatGPT Agent and Operator (OpenAI). Browser-using agents released in preview during 2025 and rolled out more broadly in 2026. Uses
OAI-SearchBotfor search andGPTBotfor broader crawling;Operatoris the user-agent-like component that performs interactions. - Perplexity Pro Search and Perplexity Agents. Perplexity is already one of the most citation-forward products on the market; its agent mode extends the same behaviour to multi-step tasks. UA:
PerplexityBot. - Google's agentic Search and Gemini Deep Research. Google's consumer-facing agent layer. This is the product Haynes calls out as the biggest mindset shift in SEO. UAs: default Googlebot for search,
Google-Extendedfor Gemini training and AI features. - Claude with web browsing (Anthropic). Used inside Claude.ai and available to developers via the Anthropic API and Claude Code. UA:
ClaudeBot. - Bing Copilot and Microsoft Copilot Search. The Bing-plus-OpenAI integration; sometimes classified as generative rather than fully agentic, but increasingly agentic in Copilot Pro.
For empirical per-engine data, CTAIO Labs ran the same article under three optimisation playbooks and measured citation deltas across ChatGPT, Perplexity, and Gemini: GEO vs AEO vs LLM-SEO — Same Content, Three Playbooks.
What to measure
Agentic search is an additional distribution channel, not a replacement for your existing metrics. Add four things to your reporting and keep the rest running.
- LLM citation count. How often your pages appear as a cited source inside the major agents, by query set. Tools in this space include Profound, Peec AI, Otterly, AthenaHQ, and Evertune. The Radar's scored shortlist is in 6 GEO Tools the Radar Actually Recommends; for hands-on test data on ten of them, see CTAIO Labs · I Tested 10 LLM Visibility Tools on 3 Real Brands.
- Branded query volume. The cleanest signal that agentic visibility is translating to brand equity is people starting to ask for your name directly. Visible in GSC and in direct traffic patterns.
- Referral traffic from AI domains. Set up channel groupings in GA4 for
chatgpt.com,perplexity.ai,gemini.google.com, andcopilot.microsoft.com. Not every agent passes a referrer, but enough do to be useful. - Conversion from AI-referred sessions. Typically low volume, unusually high intent. Watch the rate, not the raw count.
Field-tested methodology in CTAIO Labs · 10 LLM Visibility Tools on 3 Real Brands — coverage, accuracy, pricing, and the freshness problem nobody puts on the dashboard.
Field evidence from CTAIO Labs
CTAIO Labs is the practitioner surface of our network. The team runs each experiment with real budget on real brands, then publishes the methodology and the citation deltas. Use them as the empirical layer underneath the playbook above.
Related reads in this hub
Frequently asked questions
What does agentic mean?
Agentic describes a system that acts on its own to pursue a goal. In AI, it refers to a model that plans multi-step actions, uses tools, and iterates toward an outcome rather than responding to one prompt at a time. An agent is the thing; agentic is the adjective for how it behaves.
What is agentic search?
Agentic search is the use of AI agents to complete information or task-oriented goals on the web. A user describes an outcome, and the agent autonomously searches, visits pages, reads content, compares options, and returns a synthesised answer. Examples include ChatGPT Agent, Perplexity Pro Agents, and Gemini Deep Research.
Who coined the term agentic search?
In the SEO context, Marie Haynes popularised it during late 2024 and early 2025, notably in her piece 'The Agentic Web Is Here' and her follow-up on Google-Agent. The underlying technology and the term existed separately in AI infrastructure earlier, but the SEO framing traces back to Haynes.
What is the difference between agentic search and generative search?
Generative search replaces a list of links with an AI-written answer inside the search engine itself (Google AI Overviews, Bing Copilot). Agentic search goes one step further: an agent outside the search engine uses search as one of several tools, visits pages, and acts on the user's behalf. Generative search is an engine feature; agentic search is a user workflow.
Is agentic search the same as RAG?
No. RAG (retrieval-augmented generation) is a technique where a language model is given retrieved documents before generating an answer. Agent-orchestrated RAG is sometimes called 'agentic search' in AI engineering, but that is the technical meaning. The SEO meaning of agentic search is about agents browsing the open web, which may or may not use RAG under the hood.
What is the difference between GEO, AEO, LLM SEO, and agentic search optimisation?
They overlap. GEO (Generative Engine Optimization) targets the generated answers in search engines. AEO (Answer Engine Optimization) is older and focused on featured snippets and PAA. LLM SEO is a broader umbrella for optimising content to appear in LLM outputs. Optimising for agentic search is a superset of all three because agents consume generative answers, raw search results, and your page directly.
How do I optimise for agentic search?
Write a canonical answer block near the top of each page, publish an llms.txt at your root, expand Schema.org coverage beyond the minimum, make entity links explicit, ship content in server-rendered HTML, allow known AI user agents, and expose clean structured data for prices, specs, and FAQs.
Which AI agents are actually reading my site today?
The most common in 2026 are OAI-SearchBot and GPTBot (OpenAI), PerplexityBot (Perplexity), ClaudeBot (Anthropic), Google-Extended (Google's LLM training and AI features opt-out), and Bing's default crawler (for Copilot Search). Many smaller agents route through OpenAI, Anthropic, or Google infrastructure and are not visible as a distinct UA.
Will agentic search kill SEO?
No, but it changes what SEO measures. Click-through rates on informational queries drop because agents answer in-place. Brand mentions in trusted sources, citation count in AI answers, and conversions from AI-referred sessions become the relevant metrics. The craft remains: match search intent, earn trust, make content machine-readable.
What does agentic mean?
Agentic describes a system that acts on its own to pursue a goal. In AI, it refers to a model that plans multi-step actions, uses tools, and iterates toward an outcome rather than responding to one prompt at a time. An agent is the thing; agentic is the adjective for how it behaves.
What is agentic search?
Agentic search is the use of AI agents to complete information or task-oriented goals on the web. A user describes an outcome, and the agent autonomously searches, visits pages, reads content, compares options, and returns a synthesised answer. Examples include ChatGPT Agent, Perplexity Pro Agents, and Gemini Deep Research.
Who coined the term agentic search?
In the SEO context, Marie Haynes popularised it during late 2024 and early 2025, notably in her piece 'The Agentic Web Is Here' and her follow-up on Google-Agent. The underlying technology and the term existed separately in AI infrastructure earlier, but the SEO framing traces back to Haynes.
What is the difference between agentic search and generative search?
Generative search replaces a list of links with an AI-written answer inside the search engine itself (Google AI Overviews, Bing Copilot). Agentic search goes one step further: an agent outside the search engine uses search as one of several tools, visits pages, and acts on the user's behalf. Generative search is an engine feature; agentic search is a user workflow.
Is agentic search the same as RAG?
No. RAG (retrieval-augmented generation) is a technique where a language model is given retrieved documents before generating an answer. Agent-orchestrated RAG is sometimes called 'agentic search' in AI engineering, but that is the technical meaning. The SEO meaning of agentic search is about agents browsing the open web, which may or may not use RAG under the hood.
What is the difference between GEO, AEO, LLM SEO, and agentic search optimisation?
They overlap. GEO (Generative Engine Optimization) targets the generated answers in search engines. AEO (Answer Engine Optimization) is older and focused on featured snippets and PAA. LLM SEO is a broader umbrella for optimising content to appear in LLM outputs. Optimising for agentic search is a superset of all three because agents consume generative answers, raw search results, and your page directly.
How do I optimise for agentic search?
Write a canonical answer block near the top of each page, publish an llms.txt at your root, expand Schema.org coverage beyond the minimum, make entity links explicit, ship content in server-rendered HTML, allow known AI user agents, and expose clean structured data for prices, specs, and FAQs.
Which AI agents are actually reading my site today?
The most common in 2026 are OAI-SearchBot and GPTBot (OpenAI), PerplexityBot (Perplexity), ClaudeBot (Anthropic), Google-Extended (Google's LLM training and AI features opt-out), and Bing's default crawler (for Copilot Search). Many smaller agents route through OpenAI, Anthropic, or Google infrastructure and are not visible as a distinct UA.
Will agentic search kill SEO?
No, but it changes what SEO measures. Click-through rates on informational queries drop because agents answer in-place. Brand mentions in trusted sources, citation count in AI answers, and conversions from AI-referred sessions become the relevant metrics. The craft remains: match search intent, earn trust, make content machine-readable.
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