What Is an AI Agent, and How Is It Different from a Chatbot?

Imagine it is Monday morning and you run a small online fabric shop in Lagos. A customer writes that order 1042 never arrived. You give two assistants the same instruction: “Find out what happened to order 1042 and sort it out.” One of them gives you advice. The other goes and does the work, and that difference is the subject of this post.

(This is an illustrative scene, not a real business.)

What is an AI agent?

An AI agent is a system in which a language model decides for itself what to do next and uses tools to get a task done, instead of only replying with text. It can look things up, take actions in other software, check what happened, and keep going until the job is finished or it needs a person.

Two terms make that sentence easier to read. A large language model (LLM) is the kind of AI model that reads text and writes text in reply. A tool is a function or service the model can ask to use, such as a database lookup, a web search, or an email sender.

Two companies that build agents define the word in similar terms. In its December 2024 engineering post “Building effective agents”, Anthropic describes agents as systems where LLMs “dynamically direct their own processes and tool usage”. OpenAI’s guide to building agents puts it more simply: “Agents are systems that independently accomplish tasks on your behalf.” Both put the model in charge of the steps, and both give it tools to act.

How is an AI agent different from a chatbot?

A chatbot replies, and an agent acts. A chatbot is a program you talk to that answers each message you send. An agent uses a language model to steer a whole task: it picks tools, takes steps, looks at the results, and decides what to do next.

OpenAI’s guide is direct about the line. It says that applications that use a language model without letting it control how the work is carried out, “simple chatbots, single-turn LLMs, or sentiment classifiers”, are not agents. In plain words, those are programs that answer one prompt at a time, or that only label a piece of text, such as marking a review as positive or negative. Three differences follow from that:

• Who decides the next step. A chatbot answers what you asked. An agent works out what needs to happen and in what order.
• Whether it can act. A chatbot produces text. An agent can use tools to change things outside the conversation, such as updating a record or sending a message.
• Whether it repeats. A chatbot usually answers once per message. An agent works in a loop, using what each step returns to choose the next one. We follow that loop step by step in the next post.

In our reading, the boundary is not a wall. A chatbot that is given tools and allowed to choose among them for itself starts to behave like an agent. What matters is who steers.

Illustrative example. Back to the fabric shop. Assistant A, a chatbot, replies in seconds with a sensible list: check the tracking page, contact the courier, offer a refund if the parcel is lost. All good advice, and you still have to do every step yourself.

Assistant B, an agent, opens your order system and finds the tracking number. It checks the courier’s tracking page, sees that the parcel is stuck at a depot, and drafts a message to the courier. Then it stops and asks you before offering the customer a refund. It used tools, chose its own steps, and handed the money decision back to a person.

How is an AI agent different from a normal automation?

A workflow is a set of steps that people write in advance, and the software follows them in the same order every time. An agent is different because the model chooses the steps as it goes.

Anthropic draws a similar line. It says “Workflows are systems where LLMs and tools are orchestrated through predefined code paths.” In plain words, the steps are fixed in advance by software. In Anthropic’s usage a workflow may still call a language model along the way, but the path is set by code. In an agent, the model keeps control of how the task gets done.

Imagine a rule that says: whenever a new order arrives, send a confirmation email and add a row to a spreadsheet. That is a workflow, and the simplest kind, because no language model is involved at all. It does exactly the same thing each time, and that predictability is a strength. It is also why a workflow cannot cope with the order that does not arrive. Nobody wrote a rule for it. If you want to read about this kind of automation, we cover it in our guide to workflow automation with AI.

What is inside an AI agent?

In its most basic form, an agent has three parts, according to OpenAI: a model that does the reasoning and decision-making, tools that let it take actions, and instructions that say how it should behave. Think of a new employee. The model is their ability to think and read, the tools are the systems they have been given access to, and the instructions are the training and rules you hand them on day one.

Anthropic describes the same idea from another angle. It calls the basic building block of agentic systems “an LLM enhanced with augmentations such as retrieval, tools, and memory”. Retrieval means finding relevant information to read. Memory means keeping information from earlier steps or earlier conversations. We give tools and memory a post each later in this series.

As of October 2026 these two vendor definitions are the ones this series uses. Anthropic has since added a note to its post saying “Much of the tooling landscape described in this post has changed since December 2024.” We therefore use only its definitions and its two example areas, not its advice on tools. Vendors do not word the definition identically, so when you read the word “agent” in a product announcement, check whether the model is really choosing the steps or just following a script.

What kinds of tasks suit an AI agent?

Agents suit tasks where fixed rules struggle. OpenAI’s guide names three signs: complex decision-making that needs judgment or handles exceptions (its example is refund approval in customer service), rules that have become too large and tangled to maintain, and heavy reliance on unstructured data such as documents and natural language (its example is processing a home insurance claim). OpenAI adds that if a use case does not clearly meet criteria like these, “a deterministic solution may suffice”, meaning a solution that always follows fixed rules.

In its December 2024 post, Anthropic points to two promising areas for agents. The first is customer support, which it says “combines familiar chatbot interfaces with enhanced capabilities through tool integration”, for example pulling customer data and order history, or issuing refunds. The second is coding, because “Code solutions are verifiable through automated tests.” In both, success can be measured, so the agent and its builders can tell from real results whether the work is going well.

Is an AI agent always the right choice?

No. Agents are best for tasks where you cannot write the steps in advance, and they cost more than simple software. Anthropic says “Agentic systems often trade latency and cost for better task performance”. Here “agentic systems” is its umbrella term for workflows and agents, and latency means delay. It also warns that the autonomous nature of agents means “higher costs, and the potential for compounding errors”, where one early mistake can carry into every later step.

Anthropic recommends “finding the simplest solution possible, and only increasing complexity when needed.” Sometimes the simplest solution is a plain automation, and sometimes it is a single call to a language model. An agent earns its place when the path to the answer changes from case to case. In a later post we give a practical way to decide.

If you would like to talk through where AI fits in your own business, see our AI and automation services.

What to remember

• An AI agent is a system where a language model chooses its own steps and uses tools to act, rather than only replying with text.
• A chatbot answers; an agent decides, acts, checks, and continues. A workflow follows steps that people wrote in advance.
• Agents cost more and can compound mistakes, so start with the simplest tool that does the job.

Next in the series: how an AI agent actually works, step by step, from the first instruction to the final answer.