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Every course on this list teaches you to build or run AI agents: systems where a language model plans a task, calls tools and works through it with some autonomy, instead of answering one prompt at a time. Our catalogue alone lists 88 Udemy courses with agents in the title. These ten have been reviewed by 4,900 to 84,000 students each, none is rated below 4.3, and together they have more than 320,000 student reviews. They are ranked by the number of student reviews.
Every rating, review count, length, level and last-update date on this page comes from our catalogue and is refreshed with it. The pros and cons are written by CourseDuck’s editors from the course syllabus and a sample of the written student reviews, and checked against both; no course pays to be on this list. Udemy prices move with its sales, so treat the price on each card as the current one, not a fixed one.
How to pick
- No code, just the concepts: Intro to AI Agents and Agentic AI (2 hours) or AI Agents & Workflows (7 hours).
- You write Python and want to build: the AI Engineer Agentic Track for every framework, or LangChain and LangGraph for one framework in depth.
- You live in Microsoft 365: Mastering Microsoft 365 Copilot & AI Agents.
- You want to sell agents to clients without coding: AI-Agents: Automation & Business, built on Flowise.
- Ranked by the number of student reviews; every course also had to be rated 4.3 or higher and updated within the last year.
1. Intro to AI Agents and Agentic AI
The most-reviewed course on this list is also the shortest: 54 short videos, 2.2 hours, no programming. 365 Careers explains what an agent is, what separates it from a chatbot, the main architecture patterns, how agents are taught and guided, and where they fit in a business, then builds one automation in n8n so you have seen it done once. Take it if you are deciding whether agents matter for your work and want the vocabulary before you touch a tool. Developers who want code should look further down the list.
Pros
- Short videos with quick real-world examples keep concepts easy to follow
- Dedicated n8n section shows agent building in practice, not just theory
- Covers agent types and architecture patterns beyond a basic overview
- Updated August 2026, so the material reflects current agent tools
Cons
- No downloadable notes, slides, or PDFs to review after finishing
- Auto-generated subtitles lag or overlap the video for some viewers
- Glosses over agent costs, security, and hallucination risk
What students say
Yusniza Binti Mohd YusofExcellent course with well-organized content and practical insights. The lessons were clear, engaging, and highly relevant to my professional needs. I gained valuable knowledge and would definitely recommend this course to others.
Seema Pilankaragents, and prompting techniques. I gained a good understanding of AI applications, responsible AI practices, and how AI can be effectively used in business scenarios. The learning was practical, informative, and valuable for building AI awareness.
Reviews from students, via this course's page on CourseDuck.
2. LangChain- Agentic AI Engineering with LangChain & LangGraph
Eden Marco keeps re-recording sections of this course as LangChain and LangGraph change; the last update was August 2026. It goes under the hood: the ReAct loop, raw function calling, the ReAct prompt, then RAG with embeddings and vector stores, a documentation assistant, and on to LangGraph, MCP servers, agent security and multi-agent systems. Nearly 20 hours across 189 lectures. It assumes you already write Python and are comfortable with git and a debugger; it does not assume any machine learning. Because newer sections are layered onto older ones, expect the tooling and syntax to jump around between sections.
Pros
- Explains agent internals like the ReAct loop and function calling in real depth
- Updated again in August 2026 to track LangChain 1.2 and current LangGraph
- Covers MCP servers, agent security, and multi-agent systems most competitors skip
- Bestseller with 53,854 reviews and a 4.6 average rating
Cons
- Newer material is layered onto older sections, so tooling and syntax jump around
- Pace picks up sharply in later sections, leaving some concepts under-explained
- One reviewer reports rough audio and video quality
What students say
Saif Ali KhanI'm halfway through the course and so far the course has been excellent. People who are badmouthing this course saying the development environment, videos, etc. are not consistent and that the course doesn't have any structure are simply nitpickers. Since the AI/LLM landscape is changing every single day, and the LangChain framework is evolving at a rapid pace, Eden is doing a commendable job at...
Ahmed AtefThe course is great but has 3 issues 1- No LCEL implementations and types and examples and this is one of core components of langchain that should have a dedicated section for it ( i didnt see much of LCEL till now im in sec 11) 2- its very good to know old versions implementations but come on 3 sections for it( layer 1 -2 -3) , it can be all compacted to one section and give the rest of the...
Reviews from students, via this course's page on CourseDuck.
3. AI A-Z [2026]: Agentic AI, Gen AI, Prompt Engineering and RL
The oldest course here, first published in 2017 and updated since, most recently in June 2026, with parts on prompt engineering, generative AI and agentic AI now at the front and the reinforcement learning material behind them. Twelve build-along projects over 17 hours, from Q-learning through deep convolutional Q-learning, A3C, PPO and SAC to large language models. It is a survey, broad rather than deep, and reviewers report that the PPO section and an AWS Bedrock exercise no longer work as described. Take it for one map of the whole field, from prompting to reinforcement learning, with explanations reviewers repeatedly call intuition-first. Skip it if you only want to build agents this week.
Pros
- Covers prompting, generative AI, agents, and RL in one course with 12 build-along projects
- Reviewers repeatedly praise the intuition-first explanations and graphics
- End-of-video summaries and extra reading references reviewers found genuinely useful
Cons
- Some reviewers report the PPO section and an AWS Bedrock exercise no longer work as described
- No one seems to answer student questions, one reviewer says
- Breadth over depth: 10 topics in 17 hours means each one moves fast
What students say
Farbod BabaeiGreat course and very helpful to learn the beginner foundations. I love that everything is summarized at the end of each video. The videos are short and easy to understand and the tutors have kept videos short and on point. Great stuff!!!!
Alessio NegriThis course is the natural follow-up to the "Deep Learning A-Z" course, taught by the same instructors. It was truly interesting to see how Reinforcement Learning algorithms apply to practical examples. I highly recommend this course to round out your AI learning journey.
Reviews from students, via this course's page on CourseDuck.
4. AI Engineer Agentic Track: The Complete Agent & MCP Course
Ed Donner’s six-week bootcamp has you build something in five frameworks by the end: the OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and, in week six, MCP. Eight projects, including a digital twin of yourself, a sales-development agent, a deep-research agent, a stock picker in CrewAI and a four-agent engineering team. 21 hours, 132 lectures, updated September 2026, and reviewers say the GitHub materials are updated regularly and the code runs without issues. It is listed as intermediate: you should be able to read Python, and the course says a small API budget helps with the frontier-model sections, though it is optional. There are no quizzes or coding exercises built in, so you check your own work.
Pros
- Eight hands-on projects across five agent frameworks and MCP, not just theory
- GitHub materials are updated regularly and reviewers say the code runs without issues
- Ed Donner's enthusiasm and clear explanations come up in almost every review
- Covers OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP in one course
Cons
- No quizzes or coding exercises built in, so there's no easy way to check yourself as you go
- Six weeks across four different frameworks moves fast without prior Python or LLM experience
- Frontier-model sections work best with a small API budget, even though it's optional
What students say
Dalton ChanThis is an excellent course-well structured, easy to follow, and very practical. The instructor clearly explains what the code does at each step, which really helps with understanding. The inclusion of real-world projects is a big strength, as it helps bridge the gap between theory and practical application. Highly recommended for anyone looking to build both understanding and hands-on skills.
Heath UptonGreat course, absolutely worth the time and money! The code was already present which left a lot more time for explanations, background information, and other details that would have either been lost or caused the course to be 10 times longer. I went through it fairly quickly the first go, and will go through it again, hoping to go a bit deeper on the areas that appealed more to me. I recommend...
Reviews from students, via this course's page on CourseDuck.
5. AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents
The same instructor’s eight-week core track is the broader course: it starts at your first LLM product and ends at autonomous multi-agent systems, with RAG, model evaluation and QLoRA fine-tuning in between. Eight full projects again, over 20 models frontier and open-source side by side, 33 hours in all. Python is a hard prerequisite, and the course budgets about five dollars of API spend for the frontier-model weeks. Reviewers say it starts slowly and picks up once the RAG and agent material arrives.
Pros
- Eight full projects built week by week, from a RAG knowledge worker to a fine-tuned model
- Covers over 20 models, both frontier and open-source, side by side
- Reviewers repeatedly praise Ed Donner's clear, practical teaching style
- Updated as recently as June 2026, so tooling coverage is current
Cons
- Requires working Python already; not built for absolute beginners
- Course can feel slow to start before the RAG and agent material kicks in
- One reviewer felt some lectures leaned on model demos over structured teaching
What students say
Syed Muhammad AliI just Completed An LLM ENGINEERING Course Now I am Actually Say That I am Officially An LLM ENGINEER. This a great Time To spent with learning and digging In With Sir @eddonner Its pleasure to learn a lot form him its highly recommended to all other those are searching for how to become an LLM ENGINEER this is the right place where you are. Thank You Soo Much Sir @eddonner.
Hoang Minh NguyenIt was good, but I expect a more challenging final capstone project. It would be better if you focus more on RAG. Instead of just using text-based RAG, you can make it more realistic by working with additional graphs, images encoding. And the week6 is most boring, because most people will not fine-tune a frontier model. You can replace that content by the advanced RAG above. BTW, thank you from Vietnam ❤️
Reviews from students, via this course's page on CourseDuck.
6. AI Agents & Workflows - The Practical Guide
Maximilian Schwarzmüller’s practical guide is the course for people who want a mental model rather than a framework tour: what an agent is versus a workflow, how tools get attached to a model, when to steer an agent and when to build one. It covers both using agents you already have (Claude Cowork and the like) and building your own in n8n, plain Python and CrewAI, in 113 short lectures totalling 6.7 hours, refreshed in August 2026. Programming knowledge helps with the code sections but is not required. Some reviewers wanted more depth than its survey pace delivers.
Pros
- Clear, well-paced explanations reviewers say make abstract agent concepts click
- Covers both using agents like Claude Cowork and building them in n8n, Python, and CrewAI
- Refreshed in August 2026 to match current agent frameworks and terminology
- 113 short lectures make it easy to fit into a lunch break or two
Cons
- Some reviewers wanted more depth than the survey-style pace delivers
- A noticeable chunk of content covers general software concepts, not agent-specific ones
- Following the code examples goes easier with some prior Python experience
What students say
Giorgio BongiorniA very interesting course, with a lot of useful and simple examples that I can immediately use in my day-to-day activity. The teacher is very clear and explains all the details of code. Strongly adviced!
Valters PaidersThe author explained the core principles that stand behind AI agents, how they work, how they are called. Good job. Although LLM that runs locally were barely touched, author gave sufficient hooks to get more information on this.
Reviews from students, via this course's page on CourseDuck.
7. Mastering Microsoft 365 CoPilot & AI Agents [2026]
The one course on this list that is not about building agents in code. Ryan Ahmed walks through Copilot in Word, Excel, PowerPoint, Outlook and Teams, module by module, and then into building agents in Copilot Studio, with updates through 2026 for GPT-5 and Copilot Cowork. 185 lectures over 22 hours, no prior Microsoft 365 or AI experience assumed. If your company runs on Microsoft 365 and the agents you will actually use live inside it, this is the one to look at. Some reviewers find the 185 short lectures choppy.
Pros
- Covers Copilot across Word, Excel, PowerPoint, Outlook and Teams in one course
- Goes beyond writing prompts into building real AI agents in Copilot Studio
- Reviewers repeatedly call Ryan Ahmed's teaching clear and easy to follow
- Updated multiple times through 2026 to add Copilot Cowork and GPT-5 content
Cons
- 185 short lectures across 24 sections feel choppy to some reviewers
- Two reviewers say promised feature updates, like Agent Mode, never arrived
- A few students hit broken videos or stuck resource pages with no support contact
What students say
Syed Shiraz AhmedThis can truly be described as a masterclass. Ryan is absolutely exceptional, and at times I felt as though Ryan was an AI! The course was extremely easy to follow, well-structured, and highly engaging. I will certainly recommend this course to my colleagues and friends. Thank you for being such a knowledgeable, insightful, and supportive instructor.
Andrea MarinA bit too technical at times. Could have skipped the ChatGPT and Claude pieces since we have blocked those tools. The trainer said the videos would be updated when MSFT made updates to Copilot but that did not happen (updates MSFT made several months ago were not reflected). Otherwise it was good and the trainer was passionate about his work and a clear communicator.
Reviews from students, via this course's page on CourseDuck.
8. AI Agents: Building Teams of LLM Agents that Work For You
Mohsen Hassan builds one thing from start to finish: a team of LLM agents that talk to each other through AutoGen, wrapped in a Streamlit front end, deployed to Google Cloud, with an optional Stripe subscription bolted on so you could charge for it. 9.3 hours, 49 lectures, with a stock-analysis application as the worked example and an optional section on running open-source models locally. The last update was September 2025, and a few reviewers hit version mismatches and out-of-date scenarios in the notebooks. Basic Python is enough; every line of code is explained as it is typed.
Pros
- Builds one real AI Agent app end-to-end, not just isolated demos
- Optional sections cover Streamlit front-ends, Google Cloud deployment, and Stripe payments
- Reviewers praise the plain-language explanations of how agents communicate
- Covers AutoGen's chat structures in depth, 12 lectures and 175 minutes
Cons
- Some lessons pad out with the instructor reading full AI answers aloud
- A few reviewers hit version mismatches and out-of-date scenarios in the notebooks
- Needs a funded OpenAI account, though one section covers free local LLMs
What students say
Xavier SIRVENTExcellent course. Sometimes you scroll the page back and forth too quickly when showing us something.
Anonymized UserExceptionally great lectures. Full of knowledge and learning . Thank you for creating this learning experience.
Reviews from students, via this course's page on CourseDuck.
9. Agentic AI for Beginner: Build AI Agents From Scratch
Aman Kumar’s beginner course is a fast tool-by-tool tour: LangChain, CrewAI, LangGraph, Phidata and AutoGen for single and multi-agent systems, then deployment on AWS and Bedrock with guardrails, plus a newer section on observability and tracing. 5.3 hours, 63 lectures, four quizzes, updated August 2026. Reviewers like his analogies and pace; the ones who struggled were the ones with weak Python basics who hit API-key and Jupyter errors the course does not walk them through. Know a little Python before you start.
Pros
- Covers Langchain, CrewAI, Langgraph, Phidata, and Autogen in one course
- Includes AWS and AWS Bedrock deployment with guardrail concepts
- Students praise Kumar's clear analogies and approachable teaching style
- Updated in August 2026, so tooling references stay current
Cons
- Section layout confuses some students, with bonus and legacy lectures tacked on after the main flow
- No coding exercises or practice tests, so hands on drilling is light
- A few reviewers hit API key and Jupyter errors that assume more Python than the course teaches
What students say
Mahesh SrinivasanThis Course was eye opener to know about Agents and gets handson in using various frameworks. Well explained and covered whats required.
Yogesh BharadwajAwesome course to get complete clarity on agents, multi agents, tools, low or no code agentic applications etc. Thanks.
Reviews from students, via this course's page on CourseDuck.
10. AI-Agents: Automation & Business with LangChain & LLM Apps
Arnold Oberleiter’s course is the mostly no-code option on this list: despite the LangChain in the title, the building happens in Flowise, a drag-and-drop editor, which suits a non-developer and not a developer looking for LangChain code. It covers the basics of agents, LLMs, function calling and vector databases, then RAG, custom tools, a custom assistant, agents on private open-source models, and a business section on hosting, lead generation and selling agents to clients. 10 hours, 80 lectures, updated July 2026, no prerequisites. Every section ends with a summary, which reviewers single out.
Pros
- Section-by-section summaries help reviewers recall concepts before moving forward
- Covers the business side too: pricing, lead generation, and selling agents to clients
- Real-world use cases like custom AI assistants and open-source LLM agents
- Kept current, with a July 2026 update to the material
Cons
- Built almost entirely around Flowise, not LangChain despite the title
- Some sections still reflect an older Flowise version, with the newer one barely covered
- No downloadable prompt worksheets, so you retype what the instructor shows on screen
What students say
Andrei BulzanThis course was extremely informative and the instructor followed a very clear course structure with great teaching technique. Thank you!
Luka TomasevicI just started this course and for now it seams perfect easy to follow and everything is exained well for spmeone who stil hasnt got the best knowledge
Reviews from students, via this course's page on CourseDuck.
Where to go from here
The artificial intelligence category page ranks the general AI courses by student rating, and the frameworks in these courses (LangChain, LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK) are Python libraries, so Python is the other page worth a look. Our 10 best Udemy courses covers the bestsellers across every subject, and the best free Udemy courses is where to start if you would rather not pay yet.
