An AI agent doesn’t just answer a question; it carries out a task. For example, someone asks an agent to compare three providers of time-tracking software that suit a team of 20 and to request a product demo from the best fit. The agent identifies suitable providers, opens their websites, reads pricing and feature pages, compares the results, and finally fills out the demo request form. At each step, it decides what to do next based on what it found in the previous one. In its documentation for website owners, Google describes AI agents as autonomous systems that can perform tasks on behalf of people, such as booking a reservation or comparing product specifications.
At the core of an AI agent is a language model that can use tools. In its article “Building effective agents,” published in December 2024, Anthropic describes agents as systems in which language models direct their own processes and tool use. The broader term agentic AI refers to the whole field of such systems; an AI agent is a single system within it.
How an AI agent works
An agent works in a loop. According to Anthropic, agents are typically just language models that use tools based on feedback from their environment, one step after another:
- Task: A person sets a goal, as a single instruction or in a conversation.
- Plan: The model breaks the goal down into steps.
- Act: It uses a tool—for example, it opens a web page in a browser or retrieves data through an interface.
- Check: It evaluates the result, such as the content of the page it opened, and decides on the next step.
- Finish or ask: Once the goal is reached, the task ends. If the agent needs more information or a judgment call, or gets stuck, it turns to the person.
The provider decides which tools an agent has. When OpenAI introduced ChatGPT agent in July 2025, it listed a visual browser that operates websites through their graphical interface, a text-based browser for simpler lookups, a terminal for running commands, and direct access to application programming interfaces (APIs), all running on the agent’s own virtual computer. Tools can also be a company’s own interfaces. Open standards such as the Model Context Protocol (MCP), which Anthropic introduced in November 2024, connect AI applications to such tools and data sources.
Agentic features are available in many AI systems, for example in ChatGPT, in auto browse in Gemini in Chrome, in the Claude in Chrome browser extension, and in Perplexity’s Comet browser. The names and availability of such features change often: according to Google, auto browse in Gemini in Chrome is available only in the US and with a Google AI Pro or Google AI Ultra subscription (as of October 2026).
How it differs from a chat answer and a fetcher
In an ordinary chat, a person enters a prompt and gets an answer. The AI system may retrieve content from the web for it, but the result is text, and the next question again comes from the person. An AI agent, by contrast, works through several steps on its own and aims for a result outside the chat: a completed form, a reservation, a finished comparison. Anthropic names open-ended problems as the use case for agents: tasks where the number of steps required is difficult or impossible to predict and no fixed path can be set in advance.
An agent differs from a user-triggered fetcher in its role. Such a fetcher is a program run by an AI provider that retrieves a specific page because someone using an AI system asks a question, shares a link, or assigns a task; its request is what reaches the website. The agent is the system that plans the steps and operates pages: it moves between pages, clicks buttons, fills out forms, and signs in to websites if the person allows it. A single fetch for a chat answer, by contrast, only reads a page. An agent’s page requests can also reach a website as user-triggered fetcher requests—for agents hosted on Google’s infrastructure, for example, as Google-Agent.
What AI agents mean for GEO
GEO is mainly about whether and how an offering appears in the answers of AI systems—the offering’s AI visibility. AI agents can add a second question: whether an agent can use a provider’s website. When an agent compares offers or books an appointment on a person’s behalf, it matters whether it finds prices, services, and open time slots, understands a form, and can complete the task. If an agent fails on a website, the task may stop there or continue with another provider. Whether and how far visibility shifts this way is not established.
According to Google, browser agents, meaning agents that control a browser themselves, can perceive a website through screenshots, the page’s structure in the browser, and the accessibility tree—the simplified version of a page that screen readers also use. What makes a website readable and operable for them is described by the term agent-friendly website; much of it overlaps with web accessibility. Being mentioned still matters: before an agent can include an offering, it has to know about it or find it.
Limits
AI agents are a young technology. When OpenAI launched ChatGPT agent in July 2025, it wrote that the agent was still in its early stages and could make mistakes, and according to Google, Gemini in Chrome acting as an agent is an experimental feature. Anthropic points out that the autonomy of agents means higher costs and the potential for compounding errors. A mistake in one step can carry through the following ones, because each step builds on the previous one. Another known risk is hidden instructions on web pages, for example in invisible elements or metadata, that are meant to trick an agent into unintended actions. This is known as prompt injection; OpenAI and Anthropic treat it as an attack and train their agents to resist it.