What Is an AI Agent and How Does It Work?

"AI agent" has become one of the most widely used terms in technology, and also one of the most loosely defined. At its simplest, an agent is an AI system that does not just answer a question but works towards a goal by taking actions. This explainer sets out what that means in practice and where the limits sit.

From chatbot to agent

A standard AI chatbot takes a message and produces a reply. The exchange ends there, and any follow-up action is left to the person reading it. An agent goes further: it can decide what steps are needed, carry them out using software tools, check the results and keep going until the task is done or it gets stuck.

The difference is less about the underlying model and more about how it is set up. The same language model can power a simple chat window or an agent, depending on what it is connected to and how it is allowed to operate.

The four building blocks

Most AI agents can be described in terms of four parts working together:

• A model: usually a large language model that reads instructions, reasons about them and decides what to do next.

• Tools: functions the model is allowed to call, such as searching the web, reading files, querying a database, sending an email or running code.

• A goal: the task the agent has been given, often with constraints such as a deadline, a budget or rules about what it must not do.

• A loop: the cycle that lets the agent act, look at the outcome and decide on the next step, repeating as needed.

Take away the tools and the agent can only talk. Take away the loop and it can only take a single action. It is the combination that allows multi-step work.

How an agent plans and acts

When an agent receives a goal, it typically starts by breaking it into smaller steps. Asked to compile a summary of supplier contracts due for renewal, for example, it might plan to search a document folder, open each relevant file, extract the renewal dates and then write a summary.

It then works through that plan one action at a time. After each tool call, the result is fed back to the model, which decides whether the step worked, whether the plan needs adjusting, or whether the task is complete. This "observe, then decide" pattern is what lets agents recover from small problems, such as a file that is missing or a search that returns nothing useful.

Some agents also keep a form of memory, storing notes about earlier steps or past tasks so they do not have to start from scratch each time.

What agents are good at

Agents are well suited to tasks that are repetitive, involve several systems, and have a reasonably clear definition of "done". Gathering information from multiple sources, filling in routine forms, triaging support requests, and carrying out well-defined coding or data tasks are common uses.

They are less suited to work where the goal is vague, the stakes of a mistake are high, or success depends on judgement that is hard to put into words.

Limits and risks

Agents inherit the weaknesses of the models behind them. They can misread instructions, produce confident but wrong information, or choose an unsuitable tool. Because they act rather than just advise, those mistakes can have real effects, such as sending an incorrect message or changing the wrong record.

Errors can also compound. A small misunderstanding early in a long task can lead every later step in the wrong direction. Agents that read content from websites or documents can be misled by instructions hidden in that content, a risk usually described as prompt injection.

Why oversight still matters

Sensible deployments give agents only the access they need and no more. Read-only permissions, spending limits, and clear boundaries on which systems an agent may touch all reduce the damage a mistake can cause.

Many organisations also keep a person in the loop for important actions, so the agent proposes and a human approves. Logging every step the agent takes makes it possible to review what happened and why.

The bottom line

An AI agent is a model given tools, a goal and the ability to work in a loop. That combination makes it capable of handling multi-step tasks that a chatbot cannot. It also means its mistakes carry more weight, which is why careful permissions and human oversight remain an essential part of using agents well.