AI Agent vs Chatbot vs LLM: What’s the Difference?

Short answer: An LLM is the language model, the engine that predicts and writes text. A chatbot is an app that lets you talk to a model. An AI agent is a system that uses a model to pursue a goal: it picks its own steps, uses tools and keeps going until the job is done. The AI agent vs chatbot question comes down to one thing. A chatbot replies, an agent acts.

Most confusion comes from the fact that one product can be all three at once. This guide separates the parts, shows how they stack and helps you pick. If you want the full definition first, read What Is an AI Agent? and browse the best AI agents hub.

AI agent vs chatbot vs LLM at a glance

What is an LLM?

A large language model is a model trained on huge amounts of text to produce text. Give it a prompt and it returns a completion. On its own it has no tools, no memory of past jobs and no way to act in the world. It cannot open a file or send an email. It can only write.

Think of it as the engine. Every chatbot and every agent you will use today has one inside. Anthropic calls the basic building block of agentic systems an LLM enhanced with augmentations such as retrieval, tools and memory. Take those augmentations away and you are back to the bare model. The same model can sit under a plain chat window or under a full agent. What changes is what the product builds around it.

What is a chatbot?

A chatbot is a product that wraps a model in a conversation. You type, it answers, you type again. The chat window is the interface, and the model does the thinking behind it.

Chatbots vary a lot. A simple one follows a script of canned answers and may not use an LLM at all. A modern one uses a model and can hold a natural conversation. Some also search the web or run code. But the pattern stays the same: you steer every turn. If you want a table booked, the chatbot tells you how. You still book it.

What is an AI agent?

An AI agent is a system where the model runs a loop. Anthropic describes agents as “systems where LLMs dynamically direct their own processes and tool usage.” OpenAI says agents can plan and complete tasks using tools, work with other agents and keep context across steps.

In practice that means you hand over a goal, not a question. The agent plans, calls a tool, reads the result and decides what to do next. It repeats until the task is done or it needs you. Claude Code works this way on software: it reads a codebase, edits files and runs commands. Fin by Intercom works this way on support: it answers customers and can act in other systems.

How do they fit together?

They are layers, not rivals.

1. The LLM is the base. It supplies the language and reasoning.
2. The chatbot adds an interface and a conversation on top of the model.
3. The agent adds tools, a loop and memory across steps, so the model can finish work without you steering each move.

Many products sit across layers. A chat product that can search the web and run code behaves partly like an agent. That is why the labels blur. A practical test, and the one we use when we list tools on Klyvoo: after you give the instruction, did the software choose its own next step and carry it out? If yes, treat it as an agent. If you had to prompt every step, it is a chatbot.

What about agentic AI?

You will also see “agentic AI” in searches. It is a loose term for systems built from agent behavior: models that plan, use tools and act with some autonomy. An AI agent is a single thing you can point to. “Agentic” describes the style of system. In everyday use the two overlap heavily, and most writers use them interchangeably. When a vendor says “agentic,” ask what the software does without you: which tools it calls and who approves the result.

Anthropic also draws a useful line inside the idea. Workflows are “systems where LLMs and tools are orchestrated through predefined code paths”, so a developer fixes the route. Agents let the model choose the route. Both are called agentic by some vendors, but they behave differently. Workflows are more predictable. Agents cope better when the steps are unknown.

What can each one not do?

• An LLM alone cannot act. It has no tools, so it cannot look anything up or change anything unless an app around it does that for it.
• A chatbot depends on you. It will not pursue a goal across many steps without your prompts, and a scripted one cannot go beyond its script.
• An agent costs more and can go wrong in quieter ways. Anthropic notes that “the autonomous nature of agents means higher costs, and the potential for compounding errors,” and recommends testing in sandboxed environments with guardrails.

None of these replaces judgment. Keep a person on any decision that changes data, sends messages or spends money. A good habit is to let a new agent draft or suggest first, and only later let it act on its own once its work has held up on a few real tasks.

How do you choose?

Start with the job.

• You want an answer, a draft or an idea. A chatbot is enough. It is the simplest and cheapest option.
• You are building your own product on a model. You work with the LLM directly through an API, and you add tools and loops only if the job needs them.
• You want a task finished with limited supervision. Pick an agent built for that job, such as a coding agent for software or a support agent for customer questions. Compare tools on the best AI coding agents and best AI customer support agents pages.
• You are not sure. Anthropic’s advice is to find “the simplest solution possible, and only increasing complexity when needed.” Start with a chatbot and move to an agent when you hit a task it cannot finish.

Agents get their abilities from tools, and many connect to those tools through the Model Context Protocol. If that is new, read What Are MCP Servers? and browse the MCP servers directory. To build one yourself, see How to Build an AI Agent and the best AI agent frameworks.

FAQ

What is the difference between an AI agent and a chatbot?

A chatbot answers the message you send. An AI agent takes a goal and works through the steps itself, using tools along the way. With a chatbot you steer every turn. With an agent you set the goal and review the result.

Is ChatGPT an AI agent or a chatbot?

ChatGPT started as a chatbot: you ask and it answers. It behaves more like an agent when it uses tools and works through several steps toward a goal, as in ChatGPT Deep Research. The label depends on the feature you are using, not the product name.

What is the difference between an AI agent and an LLM?

An LLM is the model that generates text. An AI agent is a system built around a model, with tools, a loop and memory across steps. The agent needs an LLM to work, but the LLM alone cannot act.

What is the difference between an AI agent and agentic AI?

An AI agent is one system that pursues a goal. Agentic AI is the broader term for systems that plan and act with some autonomy. In daily use people often mean the same thing. Check what the software actually does on its own.

Do all chatbots use an LLM?

No. Older and simpler chatbots follow scripts or fixed decision trees. Modern chatbots usually run on an LLM, which is why they can hold a natural conversation.

Should I use an agent instead of a chatbot?

Only if the job needs it. A chatbot is cheaper and easier to control for questions and drafts. Choose an agent for multi-step work you can check, and start with low-stakes tasks.

Ready to compare agents by the job they do? Open the best AI agents hub. For the official thinking behind the terms, read Anthropic’s Building effective agents and OpenAI’s agents guide.