What Is Agentic Search & How to Optimize for It

Published On: 22 Sep, 2026

Search engines have long been a primary way of finding information about products and services online. In recent years, as artificial intelligence (AI) has taken center stage, search has become more conversational, allowing users to describe and refine their needs through a series of interactions. One of the latest developments in this evolution is agentic search, an AI-powered search experience that focuses on helping users achieve their search goals rather than simply returning a list of results. Simply put, agentic search uses AI to understand what a user is looking for, gather information, compare options, and make recommendations. 

Traditional vs Agentic Search Experiences 

While traditional search engines crawl, index, and rank countless web pages to pull up matching results, agentic search goes a step further. Unlike traditional search, agentic search can involve multiple steps as AI searches and evaluates information on the user’s behalf. Google’s SAGE research found that questions designed for deep-search agents required an average of 4.9 search steps, highlighting how AI can work through a query using multiple rounds of information gathering. Thus, understanding the agentic search methodology can help businesses see how AI gathers information, evaluates options, and makes recommendations. 

How Agentic Search Works 

Let’s look at how agentic search could work in a real-world scenario. Say someone searches for “running shoes.” The system could then take the following steps:

• Understand User Needs

Agentic search goes beyond the search query. When someone searches for “running shoes,” the system tries to understand what they are looking for instead of simply pulling web pages that may match the query.

• Ask Questions

If the query is too broad or vague, the AI may ask follow-up questions to better understand the user’s preferences, constraints, or priorities. In this example, it could ask about shoe size, how far they typically run, the type of terrain they run on, their preferred level of cushioning, and heel or arch support, preferred brand, budget, and other preferences.

• Gather Information

Based on the user’s answers, the system determines what information it needs to collect, such as brand, product features, fit, pricing, and other relevant information to surface options or make a recommendation.

• Compare Options

Once an agentic search has collected the required information, it can compare the available options to see which ones best match the user’s needs. It may also help the user consider additional choices, such as color or material.

• Make Recommendations

Based on the information gathered through these interactions, AI may recommend an option that appears to be a good match for the user’s needs. If the user has integrated the search platform with additional applications on his device, the system may also be able to help complete an authorized task on the user’s behalf. For instance, in the above example, depending on the platform and its integrations, the user may be able to complete the purchase after authorizing the transaction.

While this is a common way agentic search can work, recent developments in the field may make the process more iterative and ongoing, even after the initial user interaction. For instance, Google recently added a “Keep Me Updated” feature that allows search to continue monitoring a topic and provide the user with updates when new information becomes available.

What Does Agentic Search Consider When Evaluating Options? 

Agentic search can be experienced across different customer journey touchpoints, from online retailers, marketplaces, and ecommerce platforms to AI-powered search, assistants, conversational platforms, and local search channels. 

For example, Google’s recently introduced Universal Cart feature allows users to add products to a cart while browsing across Google Search, Gemini, YouTube, or Gmail. Similarly, its new Universal Commerce Protocol (UCP) is designed to help AI agents interact with retailers and commerce systems. These developments show how agentic experiences can connect product discovery with purchasing, making it important for businesses to ensure their offerings are easy for AI to understand and evaluate. 

But it’s easier said than done, because what AI needs to know about a business depends on what the customer is trying to accomplish. The table below highlights some common questions agentic search may need answers to when evaluating different options. 

Optimizing for agentic search does not mean simply applying SEO strategies to AI search. Instead, it means using SEO, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO) to provide the context, evidence, accuracy, and information AI needs to evaluate and compare brands. In simple terms, traditional SEO helps search engines discover, understand, and rank web pages, while AEO focuses on providing information that can directly answer user questions. GEO, meanwhile, aims to improve how brands and content are represented in generative AI experiences. Agentic search builds on these approaches as AI searches, evaluates, and compares information and, in some cases, acts on a customer’s behalf.  How Can Businesses Optimize for Agentic Search? Now that we understand what AI may consider when comparing and recommending options, let’s look at what businesses can do to prepare for agentic search. Clearly communicate your product/service offerings Make sure your brand information is accurate, up to date, and consistent across the digital platforms and sources AI may use when searching for options. Clearly state what products or services you offer, who they are for, what problems they solve, where and how they can be used, what differentiates your brand from competitors, and any limitations customers should be aware of. Have accurate and consistent information across digital channels Keep details such as pricing, inventory and availability, service areas, shipping, return and cancellation policies, and other important business information accurate and consistent across relevant digital channels. Regularly review these details to catch outdated or conflicting information. Be direct about information that helps decision-making Although AI may use more than on-page content to understand a website and its offerings, businesses should make key decision-making information clear and easy to find. Details such as pricing, features, use cases, industries served, compatibility, eligibility requirements, and other important information should be easy to identify Build external trust signals AI may use information beyond a brand’s own website when searching for options. Depending on your industry, strengthen trust signals such as reviews, case studies, certifications, awards, and relevant third-party recognition. Make information machine-readable Clear content structure, descriptive headings, structured data, meaningful URLs, image alt text, and well-organized internal links can help search engines and AI systems find the right information and understand how different pieces of content relate to each other. Use clear calls to action (CTA) Clear and direct CTAs can help users, as well as AI-powered search platforms, understand the next step in the customer journey. Using terms such as “Buy,” “Book,” “Request a Quote,” “Contact,” or “Schedule a Consultation” makes the intended action clear.  But optimization doesn’t stop there. Like SEO, optimizing for agentic search is an ongoing process. As AI search experiences continue to evolve, businesses need to monitor and review their brands’ online presence to check if they are accurately represented, and if their offerings are being considered by AI when they match customer needs. How to Know If Agentic Search Optimization Is Working Since agentic searches use multiple sources to collect information and credibility signals about a business, there is no single Key Performance Indicator (KPI) that can fully measure agentic search performance. However, positive answers to the following questions can indicate that your optimization efforts are moving in the right direction. 

• Visibility: Does your brand appear in relevant AI-generated responses? 
• Accuracy: Does AI describe your brand, products, or services accurately? 
• Evaluation: Does your business appear in AI evaluations or recommendations? 
• Sources: Are your website pages or external mentions being used by AI when it describes your business? 
• Conversions: Do you see an increase in AI-referred visits, calls, bookings, quote requests, or purchases? 

Final thoughts Agentic search is changing how customers can research, evaluate, and choose products and services online. As search becomes more focused on understanding customer needs and helping them make decisions, simply being visible may no longer be enough. For businesses preparing for this shift, optimizing for agentic search means building on SEO, AEO, and GEO to make their offerings easier for AI to understand, evaluate, and recommend.  

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