What Is an AI Agent? How It Works, Examples and Risks

An AI agent is software that uses an AI model to pursue a goal for you: it plans the steps, uses tools such as a browser, files or other apps, checks the results and keeps going until the task is done. A chatbot answers your question; an AI agent tries to finish the job. That extra autonomy is what makes agents useful, and also what makes them risky.

What Is an AI Agent, in Plain Terms?

Google Cloud defines AI agents as software systems that use AI to pursue goals and complete tasks on behalf of users, showing reasoning, planning and memory along with some freedom to make decisions. In its explainer on AI agents, updated on April 2, 2026, the company lists six traits: reasoning, acting, observing, planning, collaborating with people or other agents, and refining their own behavior over time.

In practice, the key word is acting. A language model on its own produces text. An agent connects that model to tools, so the text turns into actions: a search, a file edit, an email draft, a terminal command or a click in a web page.

How Does an AI Agent Work?

Anthropic, the company behind Claude, gives one of the clearest technical descriptions. In its guide Building effective agents, published on December 19, 2024, it says agents are "typically just LLMs using tools based on environmental feedback in a loop." In other words, the model picks a step, runs a tool, reads what happened and decides what to do next.

Anthropic also separates two kinds of systems. Workflows follow predefined code paths that a developer wrote in advance. Agents, by contrast, let the model direct its own process and choose which tools to use. A typical agent run looks like this:

• The user gives a command or discusses the task.
• The agent makes a plan and starts working on its own.
• After each step it checks real results, such as tool output or code that ran.
• It can pause to ask the user at checkpoints or when it gets stuck.
• It stops when the task is complete or when a limit, such as a maximum number of steps, is reached.

Google Cloud adds that an agent usually has a defined role, memory for the current conversation and past interactions, a set of tools and the underlying model that ties them together.

AI Agent vs Chatbot vs AI Assistant: What Is the Difference?

The terms are often mixed up in marketing. Google Cloud draws the line by how much the software decides on its own:

	Bot	AI assistant	AI agent
How it starts	Reacts to a trigger or command	Reacts to your request	Works toward a goal
What it can do	Follows fixed rules	Answers and handles simple tasks	Runs complex, multi-step tasks
Who decides	The script	You make the final call	It can decide independently

The line is blurring, however. Many assistants now have an agent mode, and the same app can act as a chatbot one minute and an agent the next.

What Are Examples of AI Agents Today?

Coding is the most mature use. GitHub's Copilot app can now merge a pull request on its own once the required checks pass, as we covered in our story on GitHub Copilot in Xcode. Anthropic itself cites coding agents that edit many files from a task description, plus "computer use", where Claude operates a computer to finish a task.

Agents are also moving into mainstream apps. Meta recently brought agent-style memory to everyone using its Muse agent and Meta AI, which we explained in our report on Meta Muse models. Desktop agents such as Claude Desktop can work with local files and run commands, which is why its remote task feature drew security scrutiny this week.

What Are the Risks of AI Agents?

Anthropic is blunt about the trade-off: the autonomy of agents means higher costs and "the potential for compounding errors." A chatbot's mistake is a wrong answer you can ignore. An agent's mistake can be a deleted file, a wrong payment or a bad email that already went out, and one wrong step can feed the next.

Security is the second problem. An agent that can act for you can also be pushed into acting for someone else. Researchers at Forever Security showed that a browser extension able to script claude.ai could make Claude Desktop install a package and run code, with the approval buttons clicked automatically, as detailed in our story on the Claude Dispatch flaw. No attacks in the wild were reported, but the case shows how much an agent's permissions matter. Attackers are building agent-style tools as well, as the exposed ARTEX hacking consoles found by Censys show.

How Can You Use AI Agents Safely?

Anthropic recommends testing agents in sandboxed environments with guardrails, and starting with the simplest setup that works. For everyday users, that translates into a short checklist:

• Give an agent only the accounts, folders and apps the task needs.
• Keep approval on for anything you cannot undo, such as payments, deletions and sending messages.
• Do not leave approval prompts on autopilot; read what you are allowing.
• Remove browser extensions you do not use, since they can see the same pages your agent uses.
• Check the agent's activity log after long tasks.
• Turn off remote or always-on agent features when you are not using them.

Our Take

An AI agent is less a new kind of intelligence than a new level of permission: the same models, now allowed to act. The useful question for any agent product is not how smart it is, but what it is allowed to touch and who signs off before it does. Until the security model catches up, keep agents on a short leash for anything involving money, private data or messages sent in your name.