AI agents are software systems that can reason through tasks, use tools, and take action to reach a goal without needing a human to guide every step.
Agents go beyond generating content. They research, evaluate, and compare. Increasingly, they also act. Booking, purchasing, and coordinating on a user’s behalf.
For brands, this creates a new layer of visibility. Agents are already evaluating your content, pricing, reviews, and competitors. Then they make recommendations based on their findings.
Understanding how they work matters if you want to show up where these systems are looking.
At its core, an AI agent combines a large language model (LLM) — the reasoning engine — with software tools that let it interact with the real world.
The LLM interprets goals, makes plans, and evaluates its own progress. The tools (web browsers, APIs, databases, calendars, code execution, etc.) let it act on that reasoning.
Think of the LLM as the brain and the tools as the body. The brain decides what needs to happen. The body makes it happen. An AI agent is what you get when you connect the two.
This is the difference between generative and agentic behavior.
Generative behavior generates a response and waits for your next prompt.
Agentic behavior takes your goal, figures out the steps, executes them using whatever tools are needed, and keeps iterating until it gets a result.
Most AI systems today are capable of both. Which one you get depends on the complexity of what you ask for.
How AI agents work
The core mechanism that powers every AI agent is the execution loop. It’s a cycle of reasoning and action that repeats until the task is complete.
The agent receives a goal. It makes a plan. It takes an action using one of its tools. It observes the result. Then it decides what to do next — refine the approach, try a different tool, gather more information, or deliver the final output.
Memory
Agents can also retain context across sessions — your preferences, past interactions, and ongoing tasks.
The first time you ask an agent to find noise-canceling headphones (assuming no prior conversation history), it starts fresh. But the second time, it already knows you prefer over-ear designs, you’re sensitive to weight, and you returned the last pair because the Bluetooth range was poor.
Over time, this accumulated context makes agents more effective at acting on your behalf. And more opinionated about which brands meet your standards.
Agentic reasoning vs. agentic action
Think back to the last time you used an AI tool for something more complex than generating text (e.g., researching a market, compiling a competitive analysis, or building an application).
If the tool planned an approach, gathered information from multiple sources, evaluated what it found, and iterated, you were working with agentic AI. Most of us don’t realize it because the output still came back to us for the final call.
That distinction is worth naming explicitly.
Agentic reasoning is the first layer. The agent thinks, plans, researches, evaluates, and recommends. You still make the final decision or take the final action.
When a sales director asks Gemini to analyze the competitive landscape for AI-powered CRM tools, the agent browses vendor sites, reads third-party reviews, cross-references pricing, and delivers a structured report with citations.
If you’re a CRM company, your brand just got evaluated. Your pricing clarity, review presence, documentation, authority signals across the web — all of it fed into whether the agent included you in the shortlist. And those are just some of the factors we can observe.
Agentic action is the emerging frontier. The agent doesn’t just recommend. It executes.
When a user asks their agent to plan and book a weekend trip under $800, the agent compares flights and hotels, evaluates budget fit, checks the user’s calendar, and books the best option. The user wakes up to a confirmed itinerary. For every hotel and airline in that workflow, the outcome hinged on whether the agent could access their information and complete a transaction.
Where agents fit: Generative AI, RAG, and agentic AI
Now that the mechanics are clear, it helps to zoom out and see where agents sit relative to the AI tools most marketers already use. There’s a spectrum. And understanding it clarifies which parts of your digital presence matter most for different types of AI interactions.
Generative AI is reactive
You give AI a prompt. It generates a response based on its training data. Its job ends at generation. It doesn’t take further steps without your input.
When you ask AI to rewrite a paragraph or summarize an article you’ve pasted in, that’s generative AI doing what it does best.
RAG adds external knowledge
A standalone LLM’s knowledge is frozen at the point it was trained. RAG solves this by pulling in relevant information from external sources — websites, databases, documents — and feeding it to the LLM alongside your prompt. This is how most AI search platforms retrieve current information.
When Perplexity cites recent articles in its answers, or when Google’s AI Overviews reference current webpages, RAG is doing the heavy lifting behind the scenes.
Agentic AI adds reasoning, planning, and action
As we’ve established, the key shift is from “generate an answer” to “solve the problem.” An agentic system pursues a goal, corrects its own course, and uses tools to get the job done.
This spectrum matters for brands because the more complex the user’s task, the more agentic the AI’s behavior becomes. And the more your entire digital presence comes into play.
How agents interact with your brand
When an agent evaluates your brand, it doesn’t browse your navigation or admire your hero image. It parses content programmatically, looks for structured data, and extracts the specific information it needs to complete the user’s task.
And agents don’t just read your website. They read everything about you — reviews on third-party sites, forum discussions, comparison articles, and mentions in industry publications.
Brand visibility in the agentic web operates on two levels:
- Legibility. Can the agent extract the information it needs from your digital presence? Are your pricing, service descriptions, and policies in formats a machine can parse? Or are they buried in marketing copy that requires significant processing to extract?
- Authority. When the agent has to choose between you and a competitor, what evidence exists across the web that you’re the better recommendation? Citations, reviews, expert mentions, and consistent brand information across multiple sources all feed this judgment.
This applies whether you’re in ecommerce, SaaS, professional services, content publishing, or any other space where customers research before they buy.
The principle is the same across industries: Make your information findable, parseable, and trustworthy.
The standards taking shape
Several protocols are emerging to standardize how agents interact with businesses. Here’s a brief orientation.
Model Context Protocol (MCP), created by Anthropic, is the foundational communication layer — a universal adapter between agents and the software they need to use. WebMCP extends this into the browser, letting your website declare its capabilities as structured, callable tools. It’s being developed through the W3C with backing from Google and Microsoft.
Further reading: WebMCP: What It Is, Why It Matters, and What to Do Now
Google’s Universal Commerce Protocol (UCP) and OpenAI’s Agentic Commerce Protocol (ACP) are commerce-specific standards handling the full shopping journey. UCP is co-developed with Shopify, Etsy, Target, and Walmart, backed by Visa, Mastercard, and Stripe. ACP powers checkout inside ChatGPT.
The specifics will evolve. What won’t change is what they all reward: structured, machine-readable information.
What makes the agentic web challenging
This space is moving fast. Anyone who tells you they know exactly how it plays out is selling something.
There’s no single standard yet — you can’t optimize for “agents” the way you can optimize for “Google.” Consumer adoption is real but still early. And when an agent recommends a competitor over you, there’s no equivalent of checking your search rankings to understand why.
AI visibility tools are emerging to close this gap (Semrush tracks AI citations across platforms), but the feedback loops are still developing.
The smartest approach right now is focusing on fundamentals that work across all platforms rather than betting on one. Actively monitor and experiment, and you’ll have the clearest picture of where you stand.
What this means for your brand right now
The foundations of agent readiness overlap significantly with what already drives strong SEO and AI visibility. If you’ve been investing in those areas, you have a head start.
Start with entity clarity. Can an agent confidently identify what your brand is and what it offers? Consistent brand information across the web, clear descriptions of what you offer, and authoritative citations all determine whether an agent includes you in its consideration set.
You can get a quick read on where you stand right now. Semrush’s AI Visibility Toolkit tracks how your brand is being cited across AI platforms — which models mention you, in what context, and how often. It’s the closest thing to a baseline for how agents currently perceive your brand.
From there, check your structured data. Your pricing, features, availability, policies, and credentials should be easy for a machine to find and read. Not locked inside images. Not hidden in dense copy. Not only visible to someone clicking through your site.
The easier it is for an agent to pull facts about your brand, the more likely you are to make the shortlist.
Off-site presence matters as much as your own site. Agents read reviews, comparison articles, and third-party content about you. The signals that make your brand trustworthy across these surfaces can influence whether an agent recommends you.
AI agents FAQ
What does an AI agent actually do when someone asks it to find a product or service?
The agent plans a research approach — which sites to check, what criteria matter, what “good enough” looks like. It browses vendor pages, reads reviews, compares pricing, and evaluates features against the user’s specific requirements. If the first results aren’t sufficient, it refines its approach and tries again. For brands, every step in that process is a moment where your content, your pricing clarity, and your third-party reputation either earn a recommendation or lose one.
Can AI agents make purchases without human approval?
Technically, yes. But most current implementations include confirmation steps. The trend is toward agents handling research and comparison autonomously, with human approval for transactions.
How do AI agents decide which brands to recommend?
They rely on the AI models powering them, which evaluate brands based on authority signals, structured data quality, citation patterns, and entity clarity — the same factors that drive AI visibility.
I’m already doing AI search optimization. What else do I need to do for agents?
The foundations are the same — entity clarity, structured content, authority signals. What agents add is the need for your information to be actionable, not just citable. Structured data helps because it gives agents direct access to specific facts — your price, your availability, your service area — without needing to interpret marketing language. Accurate and complete product or service information matters too. And eventually, API access or WebMCP readiness will let agents interact with your site’s functionality directly, not just read it.
Do I need to build an API for my site?
For ecommerce, API access is increasingly valuable as commerce protocols mature. For content, SaaS, and service businesses, the priority is structured data and machine-parseable information. Start with the foundations.
Are AI agents already affecting my brand’s visibility?
Almost certainly. Every time an AI platform answers a question about your industry, it’s using agentic reasoning to evaluate your brand against competitors. Whether you’re actively optimizing for this or not, it’s happening.
What happens to my website traffic when customers start using agents?
Some visits will shift from humans browsing to agents parsing. For task-oriented interactions — price comparisons, booking, procurement — agents will handle an increasing share. But humans will still visit for experiences, content, and decisions that need personal judgment. The bigger shift isn’t fewer visits — it’s that “visits” may look different in your analytics as agent-mediated traffic grows alongside human traffic.
