What Is Agentic Search? When the Buyer Sends an AI to Do the Research
Key takeaways
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Picture a VP of marketing at the end of a long day. Instead of opening twelve browser tabs to compare content agencies, she types one instruction into an AI assistant. Find five agencies that specialize in B2B SaaS, compare their pricing, services and reviews and put it in a table. Then she closes the laptop.
An AI agent does the rest. It searches, visits each agency's site, reads the services pages, looks for pricing, checks review profiles and builds the table. In the morning, she reads it over coffee and decides which two agencies get a call. The other three never know they were considered.
That's agentic search, and parts of it already exist. For B2B companies, it raises a question most websites were never designed to answer. What does your site look like to a machine that's shopping on someone's behalf? Not to a person skimming it, and not to a search crawler indexing it, but to an agent trying to complete a task and report back.
What is agentic search?
Agentic search is a form of AI-powered search in which an AI agent completes a multi-step task for a user, such as researching options, visiting websites, comparing information and sometimes taking actions like filling forms or booking, instead of simply returning links or a single answer. Examples include ChatGPT's agent mode and AI browser agents. |
From answering to doing
It helps to see agentic search as the third step in a progression. In traditional search, the user types a query, the engine returns links and the user does the work of reading and comparing. In AI search, the user asks a question and the system retrieves sources and writes an answer, so much of the reading is done for them. In agentic search, the user gives a goal, and the system plans the steps, browses multiple sites, compares what it finds and sometimes acts on it.
The jump from AI search to agentic search is a jump from answering to doing. An answer engine tells you what the options are. An agent goes and checks. That has a profound consequence for who visits your site. In traditional search, the person visits. In AI search, mostly nobody visits, because the answer is on the page. In agentic search, the agent visits, on the person's behalf, and decides what to report.
Where it is today
It's early, but it's real. OpenAI's ChatGPT can run in an agent mode that browses the web and completes multi-step tasks. Browser-based agents like Perplexity's Comet can navigate sites for the user. Google has been adding agentic features to AI Mode, such as handling reservations. The specific products change quickly, so I'd treat any list as a snapshot rather than a map.
Analysts expect the pattern to spread into business software. Gartner predicted in 2024 that by 2028, a third of enterprise software applications would include agentic AI, up from less than 1% in 2024. Procurement and vendor research are obvious places for it to show up, because they involve exactly the kind of repetitive, multi-site comparison that agents are built for.
Under the hood, agents rely on the same building blocks as AI search, namely search grounding, query fan-out and retrieval. The difference is that they string many of those steps together and can act on what they find.
What it means for how B2B buyers decide
B2B buying was already moving toward self-directed research long before AI arrived. Buyers have been doing more of their homework before talking to sales for years. Agents accelerate that shift in three important ways.
First, the shortlist gets built without you. An agent compares you with your competitors using whatever it can find, and you don't get to pitch, clarify or follow up. Second, missing information becomes a disadvantage in a way it never quite was before. If a competitor publishes pricing ranges and you say "contact us," the agent's table has a blank where your number should be, and how that blank gets interpreted is up to the agent and its user. Third, claims get cross-checked. An agent can compare what your site says with your reviews, comparison articles and forum threads in a single task, and inconsistencies surface much faster than they would with a human researcher.
None of this means humans stop buying. It means the first round of evaluation, the round that decides who even gets a call, increasingly happens without a person ever reading your homepage.
Making your site agent-ready
When I assess whether a site works for an agent as well as for a person, I ask four questions, which I remember with the word READ. Is it readable? Is it explicit? Is it actionable? And is it documented?
Readable means a machine can actually access the content. Key information should be in plain HTML, not loaded only by JavaScript, hidden in images or locked behind interactive widgets, and you shouldn't be blocking the AI crawlers and agents you want to reach you. Server-side rendering for AI crawlers and robots.txt for AI crawlers cover the technical side. Explicit means the facts are stated plainly. Who you serve, what you do, how pricing works, what's included, what you integrate with and where you operate. A person can infer a lot from design and tone. An agent can't. If it matters, write it down.
Actionable means the next step can actually be completed, with clear contact or booking paths, forms that work and plain-language calls to action. As agents begin to take actions as well as gather information, sites that are easy to act on will simply get acted on more. And documented means there's proof an agent can verify, like case studies with specifics, review profiles, third-party coverage and consistent facts across the web. Agents cross-check, so brand mentions and accurate listings matter, and consistency protects you from hallucinated facts.
The pricing question
This is the question B2B teams wrestle with most, so I'll give you my honest view. You don't have to publish a full price list. But you should publish enough for an agent to place you correctly, such as starting prices, typical ranges, what drives cost and what's included. A clear range beats a blank in a comparison table every time.
If your pricing genuinely depends on scope, say so explicitly and explain the factors. "Projects typically range from X to Y, depending on A, B and C" gives an agent, and a human, something to work with. "Contact us for pricing" gives neither of them anything, and in a world where the first evaluation happens overnight while your sales team sleeps, nothing is a risky thing to offer.
Seeing agents in your data
Agent traffic is still hard to isolate, but there are early signals worth watching. Server logs will show AI user agents and the distinctive browsing patterns of automated sessions, the territory of AI crawler server log analysis. Tracking AI platforms as their own channel in Google Analytics captures the visits that come through. And the most revealing test of all is simply to give an agent the task your buyers would, comparing us with three competitors. Then read the result as if you were the buyer. It's usually humbling, and always useful.
Don't forget the human signal either. A free-text "how did you find us?" field on your forms will start to show agent-assisted research long before your analytics can.
Agentic search in the Engine Optimization Matrix™
Agentic search stretches across every row of the Engine Optimization Matrix™. It needs the crawlability of the SEO row, the direct answers of the AEO row, the structured comparisons and off-site presence of the GEO row and the consistent entity data of the LLMO row. If any row is weak, the agent's report on you will show it, and you won't be in the room when it's read.
For related reading, see what is generative engine optimization, CMS for AI visibility, Google AI Mode for B2B and the B2B buying committee.
Buyers are asking ChatGPT for a shortlist, and soon they'll send an agent to build it. The Demand Doctrine™ for GEO is how you get on it, with structured answers, the right off-site presence and the signals generative engines trust. Out November 18. Want all four engines? Mastering AI Visibility.
Frequently asked questions
What is agentic search?
Agentic search is when an AI agent completes a multi-step research task for a user, such as searching, visiting sites, comparing options and sometimes taking action.
How is agentic search different from AI search?
AI search answers a question. Agentic search pursues a goal, planning steps, browsing multiple sites and compiling or acting on what it finds.
What are examples of agentic search?
ChatGPT's agent mode, AI browser agents such as Perplexity's Comet and agentic features in Google's AI Mode are current examples. Products in this space change quickly.
How does agentic search affect B2B marketing?
Agents may build vendor shortlists and comparisons before a person visits your site, so clear, complete and machine-readable information becomes a competitive advantage.
How do I make my website agent-ready?
Use the READ test. Make content Readable in plain HTML, state facts Explicitly, make next steps Actionable and Document claims with verifiable proof.
Should B2B companies publish pricing for AI agents?
Publishing starting prices or typical ranges, with the factors that drive cost, helps agents place you correctly in comparisons. A blank is harder to interpret.
How will I know if agents visit my site?
Check server logs for AI user agents, track AI platform referrals in analytics and ask prospects how they found you.
Will AI agents replace human buyers?
No. Agents are likely to handle more of the early research and shortlisting, while people still make the final decision.
About the author. Harold Bell is the founder of MQL Magnet and has spent about 16 years in enterprise technology marketing, working with brands like Cisco, Rubrik and Nutanix in different capacities. He's a member of the Forbes Communications Council, the creator of the Engine Optimization Matrix™ and the author of Partner Over Product. |
