What is an AI agent and how is it different from a chatbot

Over the past two years, nearly every software company has started calling its product an agent. Customer service widgets, shopping assistants, coding tools and email apps all use the word. Some of them deserve it. Many are chatbots with a new label. If you are deciding what to buy, what to build, or simply what to trust with your data, the distinction matters, because an agent can do things on your behalf and a chatbot mostly cannot.

I have spent 14 years watching technology move from labs into stores and back offices, and the pattern is familiar: the vocabulary arrives before the clear definitions do. This guide sets out what an AI agent actually is, how it works under the hood, how it compares to a chatbot, and how to tell which one you are looking at.

What is an AI agent, in plain terms?

An AI agent is a software system that is given a goal and works toward it with some independence. Instead of producing a single reply, it decides what to do next, takes an action, looks at the result, and repeats until the goal is met or it needs your input.

Most modern agents are built on a large language model (LLM), the type of AI that powers tools like ChatGPT, Claude and Gemini. An LLM is trained on huge amounts of text and is good at understanding instructions and generating language. On its own, though, an LLM only produces text. What turns it into an agent is the system wrapped around it:

• Tools: connections that let the model do things, such as search the web, read a spreadsheet, send an email, call an API or click buttons in a browser.
• Memory: a record of what has happened so far in the task, and sometimes stored knowledge about you or your business.
• A planning loop: instructions that make the model break a goal into steps and keep working through them.
• Guardrails: rules about what the agent may and may not do, and when it must ask a person for approval.

A simple example: you ask an agent to find a flight to Lisbon under a set budget and add it to your calendar. It searches several sites, compares prices, picks an option that fits your rules, asks you to confirm, books it, and creates the calendar entry. You gave one instruction; it performed a dozen actions.

How does an AI agent actually work?

The agent loop

Almost every agent, however it is marketed, runs a version of the same cycle:

1. Understand the goal. The model reads your request and any context, such as previous messages or account details.
2. Plan. It decides on a next step, or sometimes sketches a full plan in advance.
3. Act. It calls a tool: runs a search, queries a database, fills in a form.
4. Observe. It reads the result of that action, including errors.
5. Decide again. It judges whether the goal is met. If not, it adjusts the plan and goes back to step 3.

This loop is what gives agents their usefulness and their risk. Each pass is a chance to make progress, but also a chance to misread a result and head in the wrong direction.

Levels of autonomy

Not every agent runs unsupervised. In practice, most fall somewhere on a scale:

• Suggest only: the agent drafts actions and a person carries them out.
• Act with approval: the agent does the work but pauses before anything costly or irreversible, such as payments or sending messages. This is often called keeping a human in the loop.
• Act within limits: the agent works on its own inside strict boundaries, such as refunds under a fixed amount.
• Fully autonomous: rare in real deployments, and usually limited to low stakes, well defined tasks.

How is an AI agent different from a chatbot?

A chatbot is software designed to hold a conversation. Older chatbots followed scripted decision trees: press 1 for billing, type your order number. Newer ones use LLMs and can answer open questions fluently. In both cases, the chatbot's job is to respond. It waits for you, replies, and waits again.

An agent's job is to complete an outcome. Conversation is just one of its interfaces. The table below summarises the practical differences.

A useful test: ask what happens after the AI finishes speaking. If the answer is that you now go and do something, it is a chatbot. If the thing is already done, it is an agent.

Where do assistants like ChatGPT fit in?

The line is blurrier than the table suggests, because popular AI assistants have been adding agent features steadily. It helps to think of a spectrum rather than two boxes:

1. Scripted chatbot: fixed menus and keyword matching. Common on older retail and banking sites.
2. LLM chatbot: fluent conversation, but no ability to act outside the chat window.
3. Assistant with tools: can search the web, run code or read uploaded files when asked. Most mainstream AI assistants sit here today.
4. Agent: plans and executes a multistep task on its own, often across several apps, with a defined goal.

The same product can move along this spectrum depending on the mode you choose. Several assistants now offer a separate agent or task mode that can operate a browser or work through a longer job, while the default chat stays closer to level 3.

What can AI agents do today, and where do they fall short?

Realistic uses right now

• Customer support: checking an order status, processing a return, or updating an address, rather than only explaining the returns policy.
• Shopping and price research: comparing products across retailers against your criteria and assembling a shortlist or a cart for you to approve.
• Coding: reading a codebase, making changes across several files, running tests and fixing failures.
• Research and reporting: gathering information from many sources and compiling it into a structured document.
• Back office work: matching invoices to purchase orders, updating CRM records, or triaging an inbox.

Common limitations

• Compounding errors: a small misunderstanding early in a task can carry through every later step.
• Speed and cost: an agent may make dozens of model calls for one task, so it can be slower and more expensive than a single chatbot reply.
• Fragile web interaction: agents that click through websites can stall on pop ups, logins or layout changes.
• Security exposure: an agent that reads web pages or emails can be tricked by hidden instructions, a risk known as prompt injection.
• Accountability: when an agent makes a purchase or sends a message, it is not always clear who answers for a mistake.

In my experience with retail deployments, the agents that work well have narrow jobs, clear rules and an easy way to hand off to a person. The ones that disappoint were promised as general assistants that could do anything.

Should you use an AI agent or a chatbot?

Choose based on the job, not the label. A chatbot is usually the better fit when:

• People mainly need answers, explanations or drafts.
• The cost of a wrong answer is low and easy to spot.
• You do not want to grant the software access to accounts or payment details.

An agent makes more sense when:

• The task is repetitive and follows steps you could write down.
• It involves moving information between systems that do not talk to each other.
• The time saved is meaningful and errors can be caught by an approval step or reversed.

As a rough rule of thumb, not a measured figure: if a task takes a person more than a few minutes, follows a predictable pattern and happens many times a week, it is a reasonable agent candidate. If it happens rarely or needs judgment about people, keep a human doing it and use a chatbot for support.

How can you try an AI agent safely?

1. Start with read only access. Let the agent look up information before you let it change anything.
2. Require approval for money and messages. Payments, bookings and outgoing communication should need a confirmation click.
3. Grant the narrowest permissions possible. One calendar, not your whole account; one shared folder, not your entire drive.
4. Check the activity log. Good agent tools show every step taken. If a product hides this, treat that as a warning sign.
5. Test on low stakes tasks first. Run it on work where a mistake costs minutes, not money.

The simplest way to judge any product calling itself an agent: ask what it is allowed to do without you, and what it does when something goes wrong.

AI agents are a real step beyond chatbots, not just a rebrand. But the value comes from matching the level of autonomy to the task, and keeping a person close enough to catch the moments when the loop goes off course.