8 Practical AI Agent Courses for Everyone
- Nishant
- Jun 1
- 5 min read
Artificial intelligence (AI) is moving and changing. Fast. Yesterday, it was about understanding what Large Language Models (LLMs) could do. Today, it's about who can build with them. Specifically, who can create AI agents—autonomous software systems that can handle tasks, find and retrieve information, and even collaborate? If you're looking to move from an AI spectator to an AI architect, you know that simply watching demos won't cut it.
The demand for people who can build functional AI agents will quietly boom. If you're ready to get your hands dirty, we have gathered 8 practical AI agent courses for everyone who wants to acquire solid skills in AI agent development through practical exercises.
Here are the 8 practical AI agent courses for everyone:
IBM's eight-hour sprint drops you straight into retrieval-augmented generation (RAG) and LangChain. This AI agent course by IBM helps build job-ready abilities for an AI career. The curriculum will teach you about RAG, its uses, and its process alongside encoders, tokenizers, and the FAISS library.
You'll also apply in-context learning and prompt engineering to shape prompts for accurate replies and try LangChain for simpler LLM application development, including hands-on labs and a real-world project.
Learn retrieval-augmented generation (RAG) applications and processes.
Focuses on prompt engineering for precise LLM responses.
Introduces LangChain tools and components to simplify development.
Provides hands-on lab practice developing applications with LLMs, LangChain, and RAG.
Includes a real-world project suitable for job interviews.
Large language models have made their mark across many fields, including AI agents designed to interact with the world and manage multiple tasks. LLM agents will get better as LLM techniques advance, potentially changing daily life through intelligent task automation.
This AI agent course talks about fundamental concepts for LLM agents, including LLM foundations, basic abilities for task automation, and agent development infrastructures. It also covers typical agent applications like code generation, robotics, and scientific discovery while addressing current limitations and potential risks.
Covers fundamental LLM agent concepts and required abilities.
Discusses infrastructures for agent development.
Presents representative agent applications in various fields (e.g., code, robotics, medical).
Addresses limitations and potential risks of current LLM agents.
Shares insights into directions for future improvements.
This Microsoft course on AI agents teaches you to build and customize multi-agent systems where agents adopt different roles and work together on complex tasks using AutoGen, a framework for multi-agent LLM applications that teaches agents to talk, reflect, and even crack jokes.
You'll build different systems, including a two-agent comedian chat, a multi-agent customer onboarding sequence, a coding agent for financial analysis, collaborative financial analysis systems with human feedback, and more. The course provides everyone with hands-on, practical AI agent experience using AutoGen as the main part and agent-based design patterns.
Learn to make and customize multi-agent systems using AutoGen.
Enables agents to take on different roles and collaborate.
Covers design patterns like multi-agent collaboration and tool use.
Includes projects like conversational chess and coding agents for financial analysis.
Offers experience with integrating human feedback into agent workflows.
LangChain, a well-known open-source framework for LLM applications, added LangGraph, an extension for creating highly controllable agents. Basically, LangGraph adds flow control to LangChain. In this practical AI agent course, you'll learn to build an agent from scratch with Python and an LLM, then rebuild it using LangGraph, understanding its components.
It also introduces agentic search, which provides multiple answers in an agent-friendly format. You'll see how to use agentic search to give agents better data for their outputs, implement persistence for state management, and incorporate human-in-the-loop systems.
Build an agent from scratch, then rebuild it using LangGraph.
Learn about agentic search for providing better data to agents.
Implement persistence for state management across conversations.
Incorporate human-in-the-loop mechanisms into agent systems.
Develop a practical agent for an essay writing task.
Agentic workflows manage unpredictable tasks based on user input, and a serverless setup handles these efficiently without server maintenance. This AI agent course teaches you how to build and deploy a serverless agentic application, including using guardrails to protect information. You'll build agents with tools, code execution, and safety measures.
The hands-on examples include a customer service bot for a fictional business, connecting agent actions, and using Amazon Bedrock's managed services for deployment. The course highlights serverless deployment for quick scaling and responsible agent design with guardrails.
Build and deploy serverless agentic applications.
Create agents with tools, code execution, and guardrails for safety.
Use Amazon Bedrock for agent configuration and deployment.
Connect agents to services like CRMs and knowledge databases.
Implement guardrails to prevent the exposure of sensitive information and the use of inappropriate language.
Learn key ideas for designing effective AI agents and organizing a team of them to carry out complex, multi-step tasks. This practical AI agent course, featuring CrewAI's founder, helps you apply these ideas to automate six common business processes.
You'll learn essential components of multi-agent systems like role-playing, memory (short-term, long-term, shared), tools (pre-built and custom), focus (breaking down tasks), guardrails (handling errors), and cooperation (serial, parallel, hierarchical tasks). You'll use CrewAI, an open-source library, to build agent crews for tasks like customizing resumes, writing articles, and financial analysis; by the end, you'll automate six everyday business chores.
Learn principles of designing effective AI agents and organizing agent teams.
Automate common business processes using multi-agent systems.
Work with CrewAI, an open-source library for multi-agent systems.
Explore agent components like role-playing, memory, tools, and guardrails.
Build agent crews for tasks like customer support and event planning.
Hugging Face wraps theory, hands-on labs, and a leaderboard into one playful package. Build, share, and battle your agents in public. This practical AI agent by Hugging Face covers AI agents in theory, design, and practice. You'll learn to use established AI agent libraries such as smolagents, LlamaIndex, and LangGraph. An important aspect is sharing your agents on the Hugging Face Hub and trying those made by others.
The course includes challenges where you assess your agents against those of fellow students and offers a certificate upon completion of assignments. It combines foundational units on agent concepts with hands-on sessions using pre-configured Hugging Face Spaces, use-case assignments, and a competitive challenge with a leaderboard.
Study AI agents in theory, design, and practical application.
Learn to use libraries like smolagents, LlamaIndex, and LangGraph.
Share agents on the Hugging Face Hub and explore community creations.
Participate in challenges to evaluate agents against others.
Complete use-case assignments to solve real-world problems.
While LLM agents are an important AI frontier, they often lack critical skills like complex reasoning and planning for difficult problems. Building on foundational knowledge, this practical AI agent course by UC Berkeley shows advanced topics in LLM agents, concentrating on reasoning, AI for mathematics, code generation, and program verification.
It starts by introducing advanced inference and post-training methods for building LLM agents capable of search and planning. Then, it focuses on mathematics and programming applications, studying how LLMs can prove theorems and work with computer programs.
Learn advanced topics like complex reasoning and planning for LLM agents.
It focuses on AI applications in mathematics and programming.
Study how LLMs can be used for mathematical theorem proving.
Covers LLM techniques for generating and reasoning about computer programs.
Introduces advanced inference and post-training techniques for agent building.
Conclusion
The ability to not just understand AI but to build with it is becoming increasingly valuable. If you want to stop reading and start building AI agents, these 8 practical AI agent-building courses above form a clear ladder—from first prompts all the way to theorem-proving multi-agent swarms. If you're someone who desires to build AI agents that can truly help and automate, pick the slot that fits your schedule, roll up your sleeves, and ship something useful before the hype cycle spins again. The tools and knowledge are out there; it's a good time to start building.
