AI Agent: Definition, How It Works, and Examples

Detailed definition

An AI agent is a program that perceives what's happening around it, decides on an action, and carries it out, with or without human involvement at each step. This is the oldest definition of the word "agent" in artificial intelligence. It comes from Stuart Russell and Peter Norvig's reference textbook, Artificial Intelligence: A Modern Approach, which describes an agent as "anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators" (see the definition as reproduced on Wikipedia). That definition predates large language models, but it still holds up.

Major cloud providers echo the same idea in different words. For IBM, an AI agent is a system that performs tasks autonomously by designing its own sequence of steps using the tools available to it. For Google Cloud, it's an application that reaches a goal by processing an input, reasoning with the tools at hand, then acting on its decisions. Microsoft is more direct: what sets an agent apart from a plain assistant is autonomy, an assistant helps a person, an agent accomplishes a goal.

All three definitions converge on one point: an AI agent combines perception, reasoning, and action, relying on tools. They diverge slightly on where the autonomy dial sits. Google Cloud describes agents outright as autonomous software entities, while Microsoft tends to reserve the word "agent" for systems that see a goal through without validation at every step, as opposed to an assistant that stays under constant human control. An autonomous AI agent pushes this logic further still: it chooses its own route of actions and runs continuously, not just for the duration of one task.

In everyday usage, "AI agent" has also become a bit of a catch-all term. A chatbot that calls a single search function sometimes gets labeled "agent," and so does a system that chains together ten steps with full autonomy. The actual degree of autonomy, memory, and tool access varies enormously from one product to another. That's what to check before comparing two offerings, rather than trusting the word used in a brochure.

How it works

Technically, an AI agent runs on a loop that's simple to describe and harder to implement well:

1. Perception: the agent receives an input, a user message, an event, the contents of a file, a notification from another system.
2. Decision: a language model reasons over this input, compares it against its instructions and memory, then chooses an action. IBM calls this "agentic reasoning": the agent continuously re-evaluates its plan rather than following a fixed script.
3. Action: the agent executes the chosen action, often through a tool call, an external function, a search, writing to a file.
4. Observation: the result of the action becomes a new perception, and the loop starts over until the goal is reached or a limit is hit.

What sets an agent apart from a plain automated pipeline is that the sequence of steps isn't written in advance. The model itself chooses, at every turn, which tool to call and in what order. A system that always follows the same predefined sequence of steps, even if it calls a language model at one specific point, stays closer to an agent-workflow triggered by an event than to an agent in the strict sense. The nuance matters: it's what separates an autonomous AI agent from a one-off, trigger-based automation tool.

Memory plays a role too. Without it, the agent starts from scratch on every interaction. With it, the agent can remember a previous exchange, a file already processed, or a scheduled task. The actual level of memory and autonomy available depends entirely on the platform in use, not just on the language model running behind it.

A concrete example from Atako

On Atako, an agent is an AI worker created once, with a name, instructions, business context, and a model, that then runs continuously in its own isolated environment, not in a plain, one-off chat session. It isn't spun up fresh for each request: it stays active and can be reached at any time, on the channels it has been opened to, chat, email, webhooks, or a scheduled task.

Take a concrete example. A company creates a support agent and connects it to Zendesk. By default, that connection grants access to nothing: it takes an explicit "grant," a precise permission (for example, read tickets and draft a reply, but not delete a user) for the agent to be able to act. This is Atako's deny-by-default model: connecting a tool and authorizing an agent to use it are two separate actions.

Once the grant is in place, the agent perceives a new Zendesk ticket (perception), decides whether it can answer it alone or needs to escalate it (decision), then calls the corresponding action within its authorized scope (action). If it needs help on a sub-task, such as looking up a customer's history, it can delegate to a temporary sub-agent, without consuming an extra slot. Every tool call is logged in the agent's activity timeline, which makes it possible to check afterward exactly what it did.

Common mistakes

Confusing an AI agent with a chatbot. A classic chatbot answers one question at a time and forgets everything between exchanges. An AI agent can chain several steps together, call tools, and keep track of a goal.

Assuming "more autonomous" always means "better." An agent left entirely to its own devices on a sensitive task, with no permission and no oversight, is a risk, not progress. The right level of autonomy depends on the task, not on a trend.

Overlooking the permissions granted to tools. Giving an agent broad write access "just to be safe," instead of access limited to what it actually needs, is the single most common mistake companies make.

Mixing up an agent with a triggered agent-workflow. A fixed sequence of steps that calls a language model at one specific point isn't a full-fledged agent: it lacks the ability to choose its own route from start to finish.

Going further

If you want to dig into the distinction between an agent that responds to a one-off request and an agent that runs continuously toward a long-term goal, head to the autonomous AI agent page. To understand where classic generative AI stops and agentic AI begins, see Generative AI vs. Agentic AI. And for a full overview of the topic, including use cases and cost questions, the pillar page on autonomous AI agents covers every angle in detail, as does the article What Are AI Agents?.