What is an AI Agent?

Someone asks an AI assistant to find a replacement part that fits their car, confirm it is in stock, compare prices across a few retailers, and buy the best option.

That sounds like a search task, but there is more happening underneath it. The system may need to search the web, choose which sites are relevant, retrieve pages, interpret product information, change filters, compare prices, fill out forms, and possibly complete a transaction. If one route fails, it tries another.

That is where an AI agent starts to look different from a chatbot, a search engine, or a crawler. And it expands the question every site owner has to ask. It is no longer only “Can search engines find and understand this page?” It becomes “Can an AI system find it, understand it, access it, and successfully use it?”

This guide explains what AI agents are, how they work, and how they see and act on websites.

What is an AI agent?

An AI agent is a software system that uses artificial intelligence to pursue a goal, decide what actions to take, use available tools, observe the results, and keep working until it finishes the task, reaches a stopping point, or needs help.

The important distinction is that a person can specify the outcome without specifying every step. A traditional automation is told exactly what to do: open this page, click this button, enter this value, submit this form. An agent might instead just receive:

Find a cabin air filter that fits my 2019 Model X, confirm it is in stock, and show me the best-priced option.

The agent works out how to get there.

How do AI agents work?

Most agents work as a loop:

Goal → Observe → Decide → Act → Check the result → Repeat

Give an agent the request above and it may read the fitment specs, search for matching parts, compare a few results, notice the first one is out of stock, look again, and then show the best option or move toward buying it. The model matters, but the model alone is not what makes the system useful. The rest comes from what it can see, remember, access, and do.

That does not make every agent fully autonomous. Autonomy is a spectrum. Some systems freely choose their next action; others operate inside tight limits and require approval before consequential steps. Anthropic draws a similar line between predefined workflows and agents that dynamically decide how to use their tools.

What are the main parts of an AI agent?

Different systems are built differently, but most agent workflows combine some version of these parts:

• Goal or instructions. What the agent is trying to accomplish, and the limits it has been given.
• Model or reasoning system. The part that interprets the situation and works out what should happen next.
• Tools. The capabilities it can call when needed: search, a browser, APIs, databases, code, or other software.
• Memory or state. What it keeps track of, including what has happened, what it has learned, and what is left to do.
• Feedback from the environment. What it observes after each action, such as a search result, a page response, a form error, updated availability, or a tool’s reply.

This is not a fixed, universal architecture. It is a practical way to see what an agent needs beyond the model itself.

How is an AI agent different from a chatbot, crawler, or automation?

The terms chatbot, crawler, automation, and agent often get used interchangeably. They should not be.

Agent vs. automation. Automation follows a path someone already defined. An agent can determine parts of the path while it is working. That flexibility is why agents handle tasks that are hard to map in advance, and why they can be unpredictable visitors. A button moves, a product sells out, a form throws an error. A fixed automation breaks because the expected step is gone. An agent can potentially notice what changed and try something else.

Agent vs. crawler. A crawler visits pages automatically for a predefined purpose. An agent works toward a goal and chooses among actions as it goes. This matters because AI companies now run several automated systems for completely different reasons. OpenAI alone distinguishes GPTBot (potential training), OAI-SearchBot (surfacing sites in ChatGPT Search), and ChatGPT-User (certain user-initiated requests); Anthropic similarly separates ClaudeBot, Claude-SearchBot, and Claude-User. Lumping all of it together as “AI bots” hides the part that matters.

The useful question is not “Is this an AI bot?” It is “Why is this system visiting the site?”

The four categories of AI traffic, and what to do about each, are covered in the readiness guide. For now, the distinction worth holding onto is simpler — here are three requests that look similar and are not the same thing at all.

What kinds of agents visit a website?

Textbooks sort agents into reflex, goal-based, utility-based, and learning types. That is useful academically, but for website work it is more practical to think about the kind of work an agent is doing. These are not formal or mutually exclusive categories, and they overlap — a shopping agent is usually a browser agent too. Here are the five you are most likely to meet, and the shape of each one’s work.

If you want the formal version, traditional AI literature groups agents a different way:

How does an AI agent “see” a page?

A person looks at a webpage and just knows that a blue rectangle labeled “Search” is a button. Software needs a representation it can interpret. Depending on how it is built, a browser agent may use any of three: the visual rendering (what the page looks like), the DOM (the page’s underlying structure), and the accessibility tree. Google’s guidance notes agents may draw on all three when completing tasks.

The demo below shows a product page two ways: as a person sees it, then as an agent reads it. Use Remove the labels to strip the semantic markup and see what an agent can no longer tell apart.

<h1>Cabin Air Filter — Model X</h1>
<p class="price">$24.99</p>
<p>In stock</p>
<label for="qty">Quantity</label>
<input id="qty" type="number" />
<button type="submit">Add to cart</button>

<div class="title">Cabin Air Filter — Model X</div>
<div>$24.99</div>
<div>In stock</div>
<div class="box"></div>
<div onclick="addToCart()">🛒</div>

The takeaway is the overlap: many of the practices that make a site easier for assistive technology also make its controls easier for agents to understand. A visual reading is powerful but ambiguous. A human infers an unlabeled icon from context; an agent has to guess. The DOM and the accessibility tree remove that guesswork, but only when the underlying HTML is meaningful. That is the argument for semantic markup.

How does an AI agent act on a page?

Reading is only the start. What an agent can do next depends on its capabilities and permissions:

Read → Navigate and filter → Enter information → Access an account → Submit or transact

Depending on those permissions, an agent may navigate a site, apply filters, enter information, work inside authenticated areas, or complete actions such as bookings or purchases. The closer it gets to an irreversible or high-impact change, the more permissions, safeguards, and human approval matter.

But agents do not always have to click through the interface. There are two fundamentally different ways software can operate a site.

Use the interface like a person. Open the parts catalog, set the vehicle to a 2019 Model X, filter to in-stock filters, and open the matching product. This is often called browser actuation: the agent figures out how the interface works, then drives it.

Use a structured tool. The site exposes an explicit capability instead:

search_parts(
  part="cabin air filter",
  make="Model X",
  year=2019,
  in_stock=true
)

Now the agent does not have to infer what each control on the page does. The point is not that structured tools are always faster. It is that the browser agent must infer how the interface works, while the structured tool declares the available action and its inputs.

search_parts(
  part="cabin air filter",
  make="Model X",
  year=2019,
  in_stock=true
)

A few terms describe this shift. An API lets software talk to another system directly instead of reading a webpage. The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools and data. WebMCP is a proposed standard for letting websites expose structured actions directly to browser agents. Chrome describes it as experimental. None of these are ranking factors or SEO requirements; they are infrastructure that reduces how much an agent has to guess. The underlying principle is simple: the clearer a capability is to software, the less software has to guess.

What makes a page easy or difficult for an AI agent to use?

There is no “agent optimization score.” Most of the useful work is unglamorous and already familiar from accessibility and technical SEO:

• Use semantic HTML. <a> for links, <button> for buttons, <form> for forms, <label> for labels. A <div> that looks like a button works for a mouse but tells software almost nothing.
• Label forms and actions clearly. “Email address” beats “Enter value”; “Check availability” beats “Go” or a bare icon. A placeholder is not a substitute for a real label.
• Keep layouts stable. Layout shifts, expanding ads, popups, and sticky overlays move controls out from under an agent mid-task.
• Put important facts in text. A product image can complement “Cabin air filter, fits 2018–2022 Model X,” but it should not be the only place the part number and fitment exist.
• Make errors useful. “Something went wrong” gives an agent nowhere to go. “ZIP code must contain five digits” creates a path to recovery.

The flip side is what breaks an agent, and you can see most of it in the demo above: cookie walls, unlabeled controls, layout shift, client-rendering failures, and ambiguous errors. A technically public page is not automatically usable. The same friction that frustrates a person with a screen reader tends to stop an agent too. When something fails, the goal is to identify the actual layer causing it. A single 403 response does not prove every AI system is blocked. It tells you that one request was denied somewhere in the access path.

One caveat. Agent-friendly does not mean frictionless. Authentication, payments, consent, and security checks are supposed to be there. The goal is predictable, understandable interaction, not the removal of every safeguard.

What does this mean for your SEO and AEO?

Most of what makes a site usable to agents starts with work SEO teams already do. Agents add a layer on top.

SEO helps systems discover your pages and understand what they mean. Agent readiness keeps those two and adds three more questions: can the right system access the page, can an authorized agent interact with it, and are those permissions governed on purpose? That five-layer framework — the Agent Readiness Stack — is worked through in detail in the companion guide, Is your website ready for AI agents?

Traditional SEO spent decades improving how machines find and understand pages. Agents make it relevant to think about whether software can use them too, without erasing the fundamentals. Google still recommends the same things for its generative Search experiences: useful original content, technical accessibility, good page experience, and accurate structured data, and it explicitly warns against AI-specific content hacks. There is no special “AI agent schema,” and llms.txt is not required either.

One distinction is worth drawing. AI search visibility and agent readiness overlap, but they are not the same thing. A page can be easy for a search or answer engine to find, understand, and cite while still being hard for an agent to operate. The reverse holds too: a site can expose clear controls or structured actions without earning any organic visibility or AI citations.

The Discover and Access layers are their own subject: bot policy by purpose, verifying who is really visiting, measuring agent activity, and a full checklist. That is the companion guide: Is your website ready for AI agents?

Frequently asked questions

What is an AI agent in simple terms?

An AI agent is software that uses artificial intelligence to work toward a goal. It can decide what to do next, use tools such as search or a browser, observe what happens, and keep taking steps until it completes the task or needs help.

How do AI agents work?

AI agents generally work in a loop: receive a goal, observe available information, decide on an action, use a tool or interact with an environment, review the result, and repeat. More complex agents also retain state, use multiple tools, and request human approval for important actions.

What are some examples of AI agents?

Common examples include research agents that gather and compare information, coding agents that read documentation and write code, browser or task agents that operate a website’s interface, shopping agents that compare products and move toward checkout, and workflow agents that work across business tools such as CRMs and spreadsheets.

What is the difference between an AI agent and a chatbot?

A chatbot primarily communicates with a person. An AI agent can go further by pursuing a goal and taking actions through tools, APIs, or browsers. Modern assistants often combine both, which is why the distinction is becoming less visible to users.

What is the difference between an AI agent and an AI crawler?

An AI crawler automatically retrieves webpages for a predefined purpose such as search discovery or model training. An AI agent works toward a goal and can choose actions based on what happens. An agent may use web retrieval, but web retrieval alone does not make software an agent.

Can AI agents browse websites?

Yes. Some AI agents can operate browser environments, retrieve webpages, inspect the rendered interface, interpret DOM or accessibility information, follow links, enter information, and use website controls. Capabilities differ substantially between systems.

Can AI agents fill out forms or make purchases?

Some can, depending on the agent’s tools, permissions, the website, and the safeguards around the action. Consequential tasks such as purchases should include appropriate authorization and confirmation rather than assuming an agent should act unsupervised.

Do I need special AI schema or llms.txt for AI agents?

No universal AI-specific schema or llms.txt file is required. Google says neither is necessary for visibility in its generative Search experiences. Use supported structured data when it accurately represents the page, and treat emerging conventions as things to test where a real use case justifies them.

Most of the web does not need an “AI version.” It needs a good version. A site that is useful, accessible, technically sound, and clear about what its information means and what its controls do is already most of the way there. The agent layer adds new questions about access, identity, permissions, and actions, but they sit on top of the same foundation SEO has been improving for years.

If you’re working through how SEO, AEO, AI search, or agent access fits into your website strategy, feel free to get in touch.