What is Agentic Search Optimization? Is ASO the next step after SEO, AEO and GEO? And what happens when AI does more than search for information and provide answers — when it starts researching, comparing and taking action on behalf of users?
If you have been following AI Search, you have probably noticed that the conversation has already moved beyond traditional Google search results.
We now have AI Overviews, AI Mode, ChatGPT Search, Gemini, Perplexity and an increasing number of search experiences in which users receive answers synthesised from multiple sources rather than just a list of links.
The next step is even more interesting: AI is starting to do things on behalf of users.
This is where Agentic Search comes in, along with a new concept: Agentic Search Optimization (ASO).
The term is still emerging, and there is no universally accepted definition.
At Google I/O 2026, Google presented new AI-powered capabilities for Search, including agentic features that can work in the background, monitor information across the web and help users move from searching to taking action. (Google)
Academic research is also beginning to treat search agents as a distinct category of AI systems capable of planning searches, refining queries and combining information from multiple sources. (ACL Anthology)
This raises a natural question:
If AI starts making choices on behalf of users, how should we optimise our online presence?
What Is Agentic Search Optimization?
Agentic Search Optimization (ASO) is an approach to optimising an online presence for AI agents that can search, evaluate, compare and, in certain contexts, take action on behalf of users.
Until now, we have become accustomed to asking:
“How can I make AI find and mention my brand?”
In an agentic ecosystem, a new question emerges:
“What information does AI need to evaluate and select my brand?”
That is the important difference.
In an article published in August 2026, Promptwatch distinguishes between GEO and Agentic Search Optimization. GEO focuses on a brand’s visibility and mentions in AI-generated answers, while ASO also considers the next stage: how an agent uses information to compare options and continue working towards a task. (Promptwatch)
Is Agentic Search Optimization the Next Step After SEO, AEO and GEO?
ASO can be viewed as an emerging direction in the evolution of AI Search optimization, but not as a replacement for SEO, AEO or GEO.
Instead, what we are trying to optimise is expanding.
The evolution can be illustrated as follows:
SEO → AEO → GEO → Agentic Search Optimization
SEO focuses on visibility in search engines.
AEO brings direct answers and the intent behind user questions to the forefront.
GEO shifts the focus towards visibility in AI-generated answers.
Agentic Search adds another layer:
AI can use information to compare options, make a decision and, where it has access to the necessary tools, take the next step.
You can also read my article on Agentic Commerce: How AI Will Shop on Your Behalf and What Online Stores Need to Know.
ASO does not replace GEO.
Rather, ASO extends the AI Visibility conversation towards selection and action.
What Is the Difference Between GEO and Agentic Search Optimization?
GEO focuses on visibility in AI-generated answers. ASO focuses on what happens when an agent needs to use information to make a decision.
Consider this example.
A user asks:
“Who are the best business consultants in Romania?”
AI searches for information and may generate a list of recommendations.
This is an AI Search and GEO scenario.
But the user may follow up with:
“Compare them and recommend one that would be suitable for a B2B company.”
The task has now become more complex.
The agent needs to find additional information, verify it, compare the available options and determine which one best matches the user’s criteria.
In a simplified scenario, the process looks like this:
Search → Verify → Compare → Evaluate → Select
Depending on the agent’s capabilities, the process may continue:
Contact → Schedule → Book → Purchase
Not all these actions are currently available across every AI system. However, the direction is clear: Search is beginning to move towards Action.
Why Does This Change Matter for Brands?
Because being visible does not necessarily mean being selected.
An agent may find 20 companies, but the user needs one.
It may find 15 hotels, but it needs to select one that meets specific requirements.
It may identify 10 software platforms, but it needs to find the solution that best matches the user’s budget and needs.
In these situations, AI needs more than a brand name and a short description.
It needs to answer questions such as:
- What does this brand offer?
- Who is it suitable for?
- What problem does it solve?
- How does it differentiate itself?
- What are its advantages and limitations?
- How does it compare with alternatives?
- Are there reliable sources supporting its claims?
- Is the information up to date?
- What can the user do next?
This is where I believe one of the most interesting changes introduced by Agentic Search becomes apparent:
We are no longer optimising only for discovery, but also for decision-making.
How Does Agentic Search Work?
Agentic Search transforms a simple search into a broader process of research and task completion.
A traditional search engine receives a query and returns results.
An AI search system can analyse information from multiple sources, synthesise it and generate an answer.
An AI agent can go further. It starts with a user’s goal, plans its search steps, looks for additional information, verifies and compares options, and uses the findings to produce a recommendation or, depending on its capabilities, initiate or complete an action.
In simple terms, the difference is:
- Search: Finds information.
- AI Search: Understands and synthesises information.
- Agentic Search: Researches, evaluates, compares and uses information to accomplish a goal.
A 2026 study published in the ACL Anthology describes the development of LLM-based search agents that can interpret user intent, plan the necessary steps and conduct multi-stage searches. (ACL Anthology)
Another study, Agentic-R: Learning to Retrieve for Agentic Search, examines how agents can combine multi-step reasoning with information retrieval to solve complex questions.
An agent, therefore, is not simply a system that searches.
Depending on its architecture and available tools, it can become a kind of researcher + evaluator + decision-maker.
Are We Already Seeing Agentic Search in AI Products?
Yes. And this is one of the reasons why the topic deserves attention now.
OpenAI introduced Deep Research as a capability for multi-step research. The system can search, analyse and synthesise information from numerous sources, adapting its research as it progresses. (OpenAI)
Google is also developing Search towards more agentic experiences. At Google I/O 2026, the company presented Search agents that can monitor information and help users with tasks extending beyond a simple search. (Google)
Some of the infrastructure for these experiences already exists.
What Needs to Be Optimised for Agentic Search?
In an agentic ecosystem, a brand needs to be easy for AI to find, understand, verify and compare.
Information should not exist only on the brand’s website. An AI agent may draw on multiple sources, including websites, LinkedIn, social media, press coverage, directories, reviews, marketplaces and other relevant sources.
The technical side also matters: structured content, structured data, consistent information and accessibility for automated systems.
However, ASO is not just a technical issue.
An agent needs to understand the bigger picture of a brand and evaluate whether the information it finds is relevant to the user’s goal.
This is why ASO requires a coherent and verifiable online presence that helps AI understand:
- Who the brand is.
- What it offers and whom it serves.
- What makes it different.
- What evidence supports its claims.
- How it compares with alternatives.
- Whether it is suitable for the user’s objective.
ASO is not just about optimising a website. It is about optimising brand information across the entire online ecosystem.
Is Agentic Search Optimization Just Technical SEO?
No.
Technical SEO can help a system access and interpret information, but it cannot solve the entire problem on its own.
An AI agent needs to understand:
- What is this brand?
- Is it relevant to the user?
- Can it solve the problem?
- How does it compare with alternatives?
- Is there enough information to trust this option?
This is why Agentic Search Optimization intersects with several disciplines:
SEO + AEO + GEO + AI Visibility + Entity Optimization + Content Strategy + Structured Data + UX + Online Reputation.
It is not simply about “optimising for an agent”.
It is about building an online presence that an agent can find, interpret and use.
What Changes for Content Marketing in the Context of ASO?
I do not believe the answer is to produce even more content.
I believe it is becoming more important to provide the right information, structured clearly and kept up to date.
An agent comparing two services does not need copy filled with adjectives.
It needs to identify quickly:
- What each service offers.
- Who it is intended for.
- When it is suitable.
- How much it costs, if the information is available.
- What makes it different.
- What evidence supports its claims.
- What other sources say about the brand.
This is why clarity becomes increasingly important in Agentic Search.
Not only for people, but also for AI systems that need to turn information into decisions.
What Does Agentic Search Mean for AI Visibility?
AI Visibility is expanding.
Until now, we have been able to track whether a brand:
- Appears in AI-generated answers.
- Is mentioned.
- Is cited.
- Is associated with specific topics or entities.
- Is recommended.
In an agentic ecosystem, an additional question emerges:
What happens after AI finds my brand?
- Does it have enough information?
- Can it verify the information?
- Can it compare my brand with other options?
- Does it understand who my brand is suitable for?
- Can it select my brand?
These are some of the questions I believe deserve attention as AI Search evolves.
How Can We Understand the Evolution from SEO to AEO, GEO and ASO?
We can simplify it as follows.
SEO:
“Visibility in search results.”
AEO:
“Relevant answers to users’ questions.”
GEO:
“Visibility and mentions in AI-generated answers.”
Agentic Search Optimization:
“Information that AI agents can find, understand, verify, compare and use for selection and action.”
Or, more simply:
SEO: Find
AEO: Answer
GEO: Be Mentioned
Agentic Search Optimization: Evaluate → Compare → Select → Act
This is not an official definition or a standardised framework. It is a way of understanding how optimisation may evolve as Search becomes increasingly focused on understanding, decision-making and action, rather than just results.
Is Agentic Search Optimization Already an Established Standard?
No. And it is important to make this distinction.
Agentic Search is an active area of research and development. AI systems already use agents for research, search, monitoring and other complex tasks. (ACL Anthology)
However, Agentic Search Optimization as a marketing discipline is still taking shape.
Industry sources have started using the term explicitly and proposing approaches to optimisation for this type of search. (Promptwatch)
What Comes After GEO?
Perhaps the question is not:
“What is the next acronym after GEO?”
It is:
“What does AI do with the information it finds about my brand?”
We have moved from:
Search → Find
to:
AI Search → Understand → Answer
The next stage may look like this:
Agentic Search → Research → Evaluate → Decide → Act
This does not mean every stage will be automated in every situation. However, the direction is worth watching.
For brands, this means that visibility alone may no longer be enough.
Brands need to be:
Discoverable → Understandable → Verifiable → Comparable → Actionable
And this is where I see the future role of Agentic Search Optimization.
Conclusion
Agentic Search Optimization (ASO) is an emerging concept that extends the discussion around SEO, AEO, GEO and AI Visibility towards a stage where AI can research, compare, select and, in certain contexts, act on behalf of users.
For brands, the question is no longer just:
“Can AI find me?”
It is also:
“Does AI understand what I offer?”
“Can it find enough information about my brand?”
“Can it evaluate me against other options?”
And ultimately:
“Can it select or recommend me?”
This may be one of the most important changes Agentic Search brings to Marketing.
Being present in an AI answer may not be enough.
What also matters is what AI can do with the information about your brand after it finds you.
And that is where the conversation about Agentic Search Optimization begins.
Nicoleta Ion — AI Visibility Consultant | AEO & GEO | Digital Marketing & Online Communication Specialist
Sources
- Google: Search’s I/O 2026 Updates
- ACL Anthology: Yunjia Xi et al. (2026). A Survey of Large Language Model-Based Search Agents. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics.
- ACL Anthology: Wenhan Liu et al. (2026). Agentic-R: Learning to Retrieve for Agentic Search. Findings of the Association for Computational Linguistics: ACL 2026.
- OpenAI: Introducing Deep Research
- OpenAI Academy: Search and Deep Research
- Promptwatch: What Is Agentic Search Optimization?
Related article
Agentic Commerce: How AI Will Shop on Your Behalf and What Online Stores Need to Know
Nicoleta Ion – AI Visibility, AEO & GEO Consultant with 10+ years of experience in Communication, Digital Marketing and SEO. I work with professionals and brands that want to be easier to discover, understand and recommend in AI Search, from ChatGPT and Gemini to Perplexity and Google AI Overviews. I continuously research how search is evolving and test what influences visibility in AI-generated results.
My latest study – Professional AI Visibility Index Romania 2026: How AI Sees and Recommends Professionals
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