AI agents are self-governing software applications that use artificial intelligence to solve problems, make decisions, and perform tasks on behalf of users. They can adapt to circumstances, context, and data in real time, then take action with minimal human intervention — often without a customer ever visiting a website.
AI agents are on the rise, transforming how brands engage with customers as the customer journey fragments. Here's what that shift really means for brands: AI has stopped just answering questions and started taking action. And if a brand's data isn't structured, verified, and distributed to the sources AI agents trust, it won't be part of those actions.
What is agentic AI?
Agentic AI refers to the systems behind AI agents that do more than respond: they plan, reason, and take action to complete multi-step goals. Instead of following scripts, these systems understand intent, pull in context from multiple sources, and carry out tasks like booking, ordering, or scheduling with little to no input along the way.
What this looks like in practice: rather than showing a link to book a hotel, an AI agent completes the booking for you — using your preferences, past behavior, and reliable brand data across the web. It functions less like a chatbot and more like a digital assistant that can follow through.
These systems are already in use across healthcare, financial services, hospitality, and retail, and their role is only growing. Per a recent report, the agentic AI market is projected to reach $199.05 billion by 2034.
How AI agents are different from AI search engines
AI search engines and AI-driven search platforms are built to generate information and share it conversationally as they answer customer questions. AI agents go further: they don't just surface information, they act on it.
AI agents operate across four core stages:
Perception — the agent interprets the customer's intent and gathers relevant context, like past preferences, location, or details already shared in the conversation.
Reasoning — it connects that intent to structured data, APIs, and external systems, breaking the request into steps, evaluating options, and matching it to the most relevant brand information.
Action — the agent uses available tools, such as booking or scheduling systems, to deliver real-time results or complete the task.
Iteration — agents work in a loop. If the first result misses the mark, they adjust and try again, refining the outcome until the task is complete.
This ability to continuously adapt and follow through is what sets agentic AI apart from traditional search.
For example, an AI agent can help patients "find a dermatologist nearby who takes Optum insurance and specializes in microdermabrasion and adult acne treatment" in a way that's pretty similar to how an AI search engine could.
The AI agent can take it to the next level, though. After it helps customers find healthcare providers (or restaurants, shops, financial advisors, etc.), the AI agent can automatically book an appointment for that customer. In the case of the customer looking for a dermatologist, the AI agent might also collaborate with them to design an inflammation-reducing meal plan and a skin care regimen. Then, if it's been set up with the right parameters, it might purchase skin care products, automate seasonal facials, and arrange for grocery delivery based on the meal plan.
The basics of how agentic AI works
While a customer "queries" or "prompts" an AI search platform, customers "partner" or "collaborate" with AI agents. And brands definitely want to surface in those collaborations.
There are three main types of collaboration:
- Defining goals and parameters: The customer gives the AI agent instructions or a high-level set of objectives. Then, the AI agent interprets them and uses them to inform its behavior.
Financial Services Example:
"I want to update my financial portfolio so it's more conservative. When interest rates hit X%, there are significant disruptions in the global supply chain, pandemic level indicators emerge, or a nation-state in the West starts war mongering, send me an alert and advice on how to respond. Include recommendations from Vanguard, Schwab, and Scott Galloway. Show me links to all the sources you're basing your advice on, too, please."
- Delegating tasks and creating workflows: The customer gives the AI agent assistant an assignment, often defined by multi-step processes and complex considerations to achieve a goal.
Hospitality Example:
"Consult my personal and work calendars over the next six months and look back at them for the last two years. Then, help me find at least two 10-day windows this year that may be ideal for a vacation. (No product launches. No recurring doctor appts, etc.) With those windows in mind, plan three potential trips: one to the UK, one to SE Asia, and one to the American West. Focus on intermediate-level hiking, beaches or rivers, and natural areas. I want to stay in Airbnbs with 3.5 star reviews or higher. Include potential flights departing and returning to Toronto (Munro or Pearson are both fine). Red eyes preferred, at least for the first and last leg of the trip."
- Collaborating in real-time or at key intervals: The customer and the AI agent work in tandem, and the AI agent assistant adapts its actions based on feedback from the customer and/or the changing conditions surrounding their collaboration.
Retail and Direct-to-consumer Example:
"Here's a mood board. I want to create a vendor list and purchasing plan for furnishing my new 800 sq. foot apartment. I'm fine with a hi-lo mix of furniture and accessories, but I want to have the option to see each item in person before I buy. My budget is flexible, but let's target $12,500.
Organize it in a table format, please. Not a list. Keep the thumbnails, product links, and prices visible. Now turn it into a pivot table with columns for each room: Dining, Kitchen, Bedroom, Living/Workspace.
Style is on point. But less veneer and velvet. More easy-care, pet-friendly in-stock fabric options.
Can we mix in a few more budget-friendly items, especially for bedroom furniture and desks/case goods?"
AI agent examples and use cases that brands should know about
AI agents are already completing real-world tasks across industries — and increasingly, the customer journey is happening through them, not around them.
AI agents can analyze past purchases, browsing behavior, and location/travel data. Then, they can recommend products, services, and brands across digital touchpoints. They can even influence behavior when the customer travels near a brand's brick-and-mortar location.
In healthcare, an agent can schedule an appointment by checking provider availability, aligning with insurance coverage, and confirming the visit — all within a single interaction. It can also manage prescription refills by verifying dosage, checking pharmacy inventory, and placing the order without requiring a visit to a website or app.
In financial services, agents can fill out insurance quote forms, compare coverage options, and recommend policies based on a customer's needs. They can also open new accounts by verifying identity, confirming eligibility, and completing the application from start to finish.
In hospitality, agents can book hotel stays by matching preferences — like location, amenities, or loyalty programs — to verified listings, checking availability, and securing the reservation. They can also coordinate full travel plans, bringing flights, hotels, and transportation together in one step.
In retail, agents can evaluate options across multiple brands, compare product details and reviews, and complete a purchase from a trusted source. They can also track inventory, send back-in-stock alerts, and recommend products based on past behavior — even adding items to a cart when the timing is right.
Brands can also use AI agents to do things like analyze customer sentiment or predict behaviors. They can also influence customer sentiment and behavior with more personalized campaigns with relevant, enhanced content or activate dynamic pricing and inventory updates to test customer intent and drive conversions.
How AI agents use knowledge graphs to operate intelligently
The relationship between AI agents and knowledge graphs is symbiotic. Knowledge graphs provide data frameworks for AI agents. Composed of structured and unstructured data sets, knowledge graphs are like a brain filled with information. AI agents will mine, understand, and contextualize knowledge graph data when they work with customers.
AI agents are like the synapses in the nervous system. They connect the brain (or knowledge graph) to the signals that a customer using the AI agent sends. AI agents take the data in the knowledge graph and adapt to it, so it can meet the customers' goals and requests for delegation or collaboration.
AI agents interpret the knowledge graph with machine learning, NLP, LLMs, and other intelligence models. Then, they activate it. Sometimes, those actions are recommendations and insights. Sometimes, the customer has empowered the AI agent to make decisions or take action on their behalf.
Like a brain, knowledge graphs act as a brand's single source of truth. Yet, they're easy to update with new information as brand and customer data sets grow. Naturally, the better your knowledge graph, the more you can impact the quality of interactions between your brand, your customers, and your AI agent.
How Yext helps keep brands ready for agentic AI
For an AI agent to recommend a brand — and take action on a customer's behalf — three things need to be in place: accurate, structured data; consistent distribution across trusted sources; and a clear understanding of how you compare to competitors. That's where Yext comes in.
A reliable foundation of brand data. Yext's Knowledge Graph brings your brand information together in one place — organized, verified, and ready for AI. This includes structured data like hours, addresses, menus, and FAQs, as well as unstructured data like reviews and social content. Every Yext product works from this shared source of truth, giving AI agents a consistent and dependable view of your brand.
Distribution across the sources that matter. Yext helps you keep your brand data accurate across a global network of publishers — including the platforms AI agents rely on.
Intelligence that drives action. With Scout, you can see how your brand is performing across both AI and traditional search. It analyzes billions of signals to benchmark your visibility and highlight where to focus. Instead of automating generic tasks, your teams can prioritize the actions most likely to improve performance in the moments that matter.
AI agents are quickly becoming a central part of how customers discover and choose brands. As that shift continues, your data plays a more direct role in performance — shaping whether you're surfaced, trusted, and selected.
If you're exploring how AI agents fit into your strategy, or want to understand how your brand shows up today, start now by getting your Brand Visibility Score with Yext Scout.