Quick answer

An AI agent is different from a regular chatbot because it can take actions, not just generate text: it can decide which tool to use, call an API, search for information, remember context across steps, and chain multiple actions together to complete a task, rather than just responding once to a single prompt. Microsoft’s free LangChain.js for Beginners course and IBM’s open LangChain-powered agent course are both genuinely good free starting points that build a real, working agent rather than just explaining the concept.

“Agentic AI” has become one of the most talked about terms in AI over the past couple of years, and also one of the most loosely used. This guide is about the concrete, technical difference between an agent and a chatbot, and where to actually learn to build one for free.

What actually makes something an agent

A standard chatbot takes an input and generates a text response. That’s the whole loop. An agent does more: it can decide, based on the task, which tool or action to take next, whether that’s searching the web, calling an API, running a calculation, or querying a database, and it can chain several of these steps together, using the result of one step to decide the next one, before finally responding.

  • Tool use: the ability to call external functions or APIs, like a search tool, calculator, or database query, rather than only generating text.
  • Memory across steps: keeping track of what’s already happened in a multi-step task, rather than treating each step as if nothing came before it.
  • Decision making: choosing which action to take next based on the current situation, rather than following a fixed, predetermined script.
An agent’s core capability is deciding what to do next and which tool to use, not just generating a single text response.

How agentic AI systems are actually built in practice

Most real-world AI agents today are built using frameworks like LangChain and LangGraph, which handle a lot of the underlying plumbing, connecting a language model to tools, managing memory, and orchestrating multi-step workflows, so you’re not building all of that from scratch. LangGraph specifically is built for orchestrating more complex, stateful agent workflows where an agent might loop, backtrack, or hand off between multiple specialized sub-agents.

  • LangChain: a widely used framework for connecting language models to tools, data sources and memory, the foundational layer most agent tutorials build on.
  • LangGraph: built specifically for orchestrating more complex agent workflows, including multi-agent systems where different agents handle different parts of a task.
  • Multi-agent systems: setups where multiple specialized agents work together on different parts of a task, an increasingly common pattern for more complex applications.
LangChain handles the connections to tools and data. LangGraph handles orchestrating more complex, multi-step agent behavior on top of that.

The best free courses to actually build an agent

  • Microsoft’s LangChain.js for Beginners: a free, eight-chapter course with over seventy runnable code examples, deliberately teaching tools and agents early rather than saving them for later, since that reflects how production agent systems actually work.
  • IBM’s LangChain-powered agent course: an open, free course walking through building an agent that pulls data from APIs, uses memory, and responds contextually, with real integration examples.
  • DeepLearning.AI’s agentic AI courses (several free): cover building agentic systems with LangChain and LangGraph specifically, with a hands-on, project-based structure.
These courses differ mainly in language and framework focus, but all get you building a real, working agent rather than just explaining the theory.
Agent skills build on top of API and prompting fundamentals, not instead of them.

Where agent skills fit with everything else in AI development

Building agents assumes you’re already comfortable working with language models directly, calling APIs, and handling basic prompting; see our guide to free AI API courses for developers if that foundation isn’t solid yet. For the broader picture of building real applications around LLMs, including but not limited to agents, our guide to free LLM application development courses covers that wider territory.

Ready to build something that takes real action, not just generates text? Browse today’s free generative AI courses.

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Frequently asked questions

What’s the actual difference between an AI agent and a chatbot?

A chatbot takes an input and generates a single text response. An agent can take actions, like calling an API, searching for information, or using a tool, decide what to do next based on the situation, and chain multiple steps together to complete a task, rather than just responding once.

What tools do people actually use to build AI agents?

LangChain is the most widely used framework for connecting a language model to tools, data sources and memory. LangGraph builds on top of that specifically for orchestrating more complex, multi-step agent workflows, including systems with multiple specialized agents.

What’s the best free course to actually build an AI agent?

Microsoft’s free LangChain.js for Beginners course and IBM’s open LangChain-powered agent course are both genuinely hands-on, walking you through building a real, working agent with actual code rather than just explaining the concept.

Do I need to know how to code to learn about AI agents?

To build one, yes, basic programming comfort is expected, since most agent courses are code-first. If you just want to understand the concept without building one yourself, general AI literacy content covers agents at a conceptual level without requiring code.

What is a multi-agent system?

A multi-agent system uses several specialized AI agents that each handle a different part of a larger task, coordinating with each other rather than relying on one single agent to do everything. It’s an increasingly common pattern for more complex, real-world applications.