We earn a commission if you buy through links on this page. How we're funded.
Quick answer
Agentic AI, the DeepLearning.AI course Andrew Ng teaches on Coursera, is a current, hands-on course on building AI agents in Python, and we rate it 4.1 out of 5: five modules on reflection, tool use and the Model Context Protocol, evaluation and error analysis, planning and multi-agent systems, about 23 hours by Coursera's module cards. It is for developers who already write intermediate Python and have called a language model through an API. It is not for non-coders, and not for anyone who needs a professional certificate: it ends in a single-course certificate, and it is bought on its own because Coursera Plus does not include it.
Why we score it 4.1 / 5
A DeepLearning.AI course taught by Andrew Ng on building agentic AI systems in Python: reflection, tool use and the Model Context Protocol, evaluation and error analysis, planning and multi-agent workflows, across five modules with three graded programming assignments. We value how current it is, recently updated in September 2026, and the hands-on practice packed into 23 hours by Coursera's module cards. What holds the score down is the credential and the cost: a single-course certificate rather than a professional one, bought per course and not included in Coursera Plus, with no learner rating published yet.
4.6 / 5 how well it teaches4.0 / 5 what the certificate is worth
Curriculum currency 4.9 · Completion realism 4.4 · Skill value 4.6 · Employer recognition 4.0 · Cost & value 3.0 · Salary impact 3.7 — the score is the average of these six, each out of five.
Scored with AI assistance against our published rubric; the editor is responsible for the rubric and for every published score.
Check price & enrol on Coursera →
This review covers what DeepLearning.AI's Agentic AI course teaches in each of its five modules, what it costs and asks of you, how it compares with the other agent courses we review, and who should take something else. Everything in it comes from the course's Coursera page as it stood on 8 October 2026.
What is the Agentic AI course?
Agentic AI is a single course from DeepLearning.AI on Coursera, taught by Andrew Ng, on building software in which a language model plans and carries out several steps of a task instead of answering one prompt. Coursera's page data dates its launch on the platform to 14 September 2026, and its details box marks it “Recently updated!” in September 2026.
It has five modules, a quiz at the end of each, three graded programming assignments and a set of ungraded labs. Coursera lists it as Intermediate and gives its recommended experience as “Intermediate Python skills and a basic understanding of large language models and APIs.” It is a course, not a programme: there are no member courses and no capstone, and it ends in a shareable Coursera course certificate.
The design choice that defines it is stated in its own description: it builds each pattern “from first principles before exploring frameworks”, in Python. None of its module descriptions names LangChain or LangGraph. What it teaches instead are the patterns those frameworks package — reflection, tool use, planning and multi-agent collaboration — written in plain code (the tool-use module works with the AI Suite library), so that you can see what a framework is doing when you later use one.
What you learn, module by module
Coursera lists the five modules in this order, under these titles:
- Introduction to Agentic Workflows. What makes a system agentic, why autonomy is a spectrum rather than a yes-or-no, and task decomposition — breaking a real process such as invoice processing or a customer-service reply into steps a model and its tools can carry out. It ends with a research agent you can try.
- Reflection Design Pattern. Having a model critique and revise its own output, or react to external feedback such as an error message. The worked examples generate charts and improve generated SQL, and the module closes with the first graded lab.
- Tool use. Function calling: exposing functions as tools, detecting when the model asks for one, running it and feeding the result back. It covers code execution, an email-assistant workflow and an introduction to the Model Context Protocol (MCP), the open standard for connecting models to tools and data. The second graded lab is here.
- Practical Tips for Building Agentic AI. Evals, error analysis on traces, the difference between end-to-end and component-level evaluation, and tuning a workflow for quality, latency and cost.
- Patterns for Highly Autonomous Agents. Planning, with plans written as JSON or as code, and multi-agent workflows in linear, hierarchical and all-to-all arrangements. It ends with the third graded assignment, on agentic workflows.
Module 4 is the one to notice. Whether an agent succeeds is a measurement problem: the same request can produce a different chain of tool calls each time, and both can look plausible. Many agent courses treat evaluation in passing; this one gives it a module of its own, and the first module already presents evals and error analysis as what drives improvement. That emphasis is the part of the syllabus an employer is most likely to probe in an interview.
What it leaves out is as clear. There is no module on retrieval-augmented generation — DeepLearning.AI teaches that in a separate course, Retrieval Augmented Generation (RAG) — and no framework is taught in depth. If a job advert names LangGraph or a specific agent SDK, you will still have that framework to learn — though with the patterns already understood.
Cost, time and what you need first
Time. About 23 hours by Coursera's module cards, and the page data's own total for the course content agrees. Coursera's headline gives a pace instead, two weeks at ten hours a week. Coursera's module contents put most of those hours into labs — three graded programming assignments and seven ungraded labs — rather than video, so a learner who skips the ungraded labs will finish sooner and learn less.
Cost. It is bought per course on Coursera, priced by country, and Coursera Plus does not include it: its page shows no “Included with Coursera Plus” line, and its own data marks it outside Plus (checked 8 October 2026). Coursera's FAQ says the course materials, graded work and certificate come with buying the certificate, and that some learners may be offered a free trial first. Coursera's financial aid is a discount decided course by course, and Coursera says a decision can take up to 16 days.
What you need first. Take the recommended experience literally. If you can write a function, read a traceback, install a package and have sent a prompt to a model through its API, you are ready. If you have not, the graded labs will be learning Python and learning agents at once, and a shorter introduction to LLM applications is the better first step.
Certificate. A shareable Coursera course certificate from DeepLearning.AI. It records that you completed the course; it is not a proctored certification, and no vendor exam is attached to it.
Learner evidence. None yet. When the page was read on 8 October 2026 it showed 5,002 enrolments and no learner rating, which is what a course a few weeks old looks like. That is a reason to read the module outlines above closely, not a reason for concern.
Check the price & enrol on Coursera →How we checked this. We list the cost as Paid: bought per course, priced by country; not included in Coursera Plus. Source: Coursera course page coursera.org/learn/dlai-agentic-ai, read server-side 2026-10-08: the button reads "Enroll now", the page shows no "Included with Coursera Plus" line and its own data marks it outside Coursera Plus; its product type in the page data is the same as Generative AI for Everyone's, which is bought per course. The page states no price: Coursera prices by country, and this course's enrol screen has not been read in a browser, so no figure is published. Where we give a figure, the source says when we read it on the provider's page. We re-read prices by hand and publish no figure we cannot source — where a provider prices regionally, we say so rather than quote a number that is wrong for most readers.
Pros and cons
✓ What we liked
- Current: updated in September 2026, with MCP, evals and multi-agent patterns
- Teaches the patterns underneath agent frameworks, in plain Python
- A full module on evaluation and error analysis
- Three graded programming assignments as well as ungraded labs
- Taught by Andrew Ng, in about 23 hours
✕ What to keep in mind
- Needs intermediate Python and some experience calling LLM APIs
- A single-course certificate, not a professional certificate
- Bought on its own; Coursera Plus does not include it
- No learner rating published yet
Not sure this is the right one for you?
Tell the picker about your background and what you want the certificate to do, and it narrows the list to the one or two courses we would start with. It suggests only our affiliate partners’ courses, and says so before it suggests anything.
Try the AI Certification Picker →Who should take it, and who should not
Take it if you are a developer or data scientist who already builds with language models and wants to understand agents properly before committing to a framework; if you have used an agent framework and want to know what it is doing underneath; or if evaluation is the part of agent work you know you are missing.
Skip it if you do not write Python yet — start with Python and a first LLM application, and come back. Skip it, too, if what you need is a credential an employer filters on: a single-course certificate will not carry that weight, and the proctored agent exams on our agentic AI certifications page are the route for that. And if you only want to understand what agents are, without building one, our plain-English explainer covers it at no cost.
How it compares with the agent courses we score
The table below compares five agent and LLM-engineering courses on provider, level, time, coding, how you pay and our rating. All five are Intermediate and use Python; they run from about 20 hours to 188.
| Course | Provider | Level | Time | Coding | How you pay | Our rating | Enrol |
|---|---|---|---|---|---|---|---|
| Agentic AI (DeepLearning.AI) | DeepLearning.AI (Coursera) | Intermediate | ~23 hrs | Python | Per course; not in Coursera Plus | 4.1 / 5 | Coursera → |
| AI Engineer Agentic Track: The Complete Agent & MCP Course | Udemy | Intermediate | ~21 hrs | Python | Single purchase | 3.7 / 5 | Udemy → |
| LangChain: Agentic AI Engineering with LangChain & LangGraph | Udemy | Intermediate | ~20 hrs | Python | Single purchase | 3.9 / 5 | Udemy → |
| AI Agent Engineer (365 Data Science) | 365 Data Science | Intermediate | ~36 hrs | Python | Subscription | 3.6 / 5 | 365 Data Science → |
| IBM Generative AI Engineering | IBM (Coursera) | Intermediate | ~188 hrs | Python | Coursera subscription | 3.8 / 5 | Coursera → |
The Udemy agent courses. Ed Donner's AI Engineer Agentic Track and Eden Marco's LangChain course teach agents through frameworks — the OpenAI Agents SDK, CrewAI and LangGraph in the first, LangChain and LangGraph in depth in the second — and each is bought once. This course goes the other way, building the patterns in plain Python before any framework, and it has graded programming assignments, which the Agentic Track's curriculum does not. Choose a Udemy course if you want a framework you can put on a CV next week; choose this one if you want to understand what the framework is doing.
365 Data Science's AI Agent Engineer. A ten-course track on a subscription, from agent architecture and MCP through LangChain and LangGraph to a course on evaluating agents, with a final exam before its certificate. It is broader and assessed; this course is shorter and goes deeper on the patterns themselves.
IBM Generative AI Engineering. If you are starting further back, with no Python or LLM applications yet, IBM's sixteen-course Professional Certificate builds the whole stack from Python through retrieval and LangChain, and ends in a better-known credential. It asks far more time; this course is the shorter step to take once you can already build with a model's API.
Is DeepLearning.AI's Agentic AI course worth it?
Yes, for the reader it is built for. We rate it 4.1 out of 5. It is current to within weeks, it teaches the patterns every agent framework is built from, it gives evaluation and error analysis the module they deserve, and its three graded assignments make you write the code rather than watch it written. What holds the score down is what you get at the end and how you pay: a single-course certificate that will not filter you into an interview on its own, bought on its own, since Coursera Plus does not include it, with no learner rating yet to set beside our reading of the syllabus.
The practical advice is the same as for any agent course: the certificate is a receipt, and the thing to show an employer is an agent you built, instrumented and wrote up — including where it failed. This course's evaluation module is unusually good preparation for that write-up, so use it on a project of your own as well as on the labs.
Ready to start?
Paid through Coursera rather than through the provider, and not included in Coursera Plus. Coursera prices it by country.
Frequently asked questions
Is DeepLearning.AI's Agentic AI course worth it?
Yes, if you already write intermediate Python and have called a language model through its API. We rate it 4.1 out of 5: it is current to within weeks, it builds reflection, tool use, planning and multi-agent patterns in plain Python, and it gives evaluation and error analysis a module of their own.
What holds it back is the credential and how you pay. It ends in a single-course certificate, which records completion rather than proving skill to an employer, and it is bought on its own because Coursera Plus does not include it.
So take it for the understanding, and plan a project of your own alongside it. An agent you built, measured and wrote up — including where it failed — is what an interviewer will ask about, and the course’s evaluation module is good preparation for exactly that write-up.
How long does the Agentic AI course take?
About 23 hours by Coursera's module cards, across five modules. Coursera’s headline gives a pace instead, two weeks at ten hours a week, and the page data’s own content total agrees with the module cards.
Most of those hours are labs rather than video: three graded programming assignments and seven ungraded labs, against about three hours of lectures. Skipping the ungraded labs gets you to the end sooner, but they are where the patterns turn into habits.
Coursera describes the schedule as flexible, at your own pace, so plan by the hours rather than by the weeks, and leave room for the graded assignments, which are the longest single items in the course.
Do I need to know Python for the Agentic AI course?
Yes. Coursera’s recommended experience is “Intermediate Python skills and a basic understanding of large language models and APIs”, and three of the five modules include a graded programming assignment.
A fair test: if you can write and call functions, work with dictionaries and lists, install a package and read a traceback, and you have sent a prompt to a model through its API, you are ready. The course builds its patterns in plain Python rather than inside a framework, so there is little for an unfamiliar library to hide.
If you are not there yet, learn Python and build one small LLM application first. If you want the concepts before the code, Generative AI for Everyone explains them without any programming.
Is the Agentic AI course included in Coursera Plus or not?
No. Its Coursera page shows no “Included with Coursera Plus” line, and the page’s own data marks it outside Plus (checked 8 October 2026). It is bought on its own, and Coursera prices it by country.
Coursera’s FAQ for the course says the materials, graded work and certificate come with buying the certificate, and that some learners may be offered a free trial first. Financial aid is a discount you apply for from the course page, and Coursera says a decision can take up to 16 days.
DeepLearning.AI’s courses differ here: Generative AI for Everyone is outside Coursera Plus as well, while Generative AI with Large Language Models is included. Check the “Included with” line on each course page rather than assuming.
Does the Agentic AI course teach LangChain or LangGraph?
Not by name. None of its five module descriptions mentions LangChain or LangGraph. The course says it builds each pattern “from first principles before exploring frameworks”, and its tool-use module works with the AI Suite library and introduces the Model Context Protocol.
That is a deliberate choice, and for many learners a good one. Reflection, tool calling, planning and multi-agent coordination are what every agent framework packages; once you have written them in plain Python, a framework’s abstractions are easier to read and its failures easier to debug.
If a job advert names LangGraph, you will still need to learn it. Our agentic AI certifications guide covers the framework courses, including a Udemy course on LangChain and LangGraph.
Is the Agentic AI certificate a certification?
No. It is a shareable Coursera course certificate from DeepLearning.AI, which records that you completed the course and its graded work. There is no proctored exam, and it is not tied to any vendor’s certification.
That matters for how you use it. A course certificate belongs under training on a CV; it shows initiative and a current syllabus, but no hiring system filters on it.
Proctored agent credentials do exist now, from NVIDIA, Microsoft, GitHub and LangChain among others, and our agentic AI certifications page compares them. Taking a course like this one first and sitting an exam later is a sensible order.
Keeping this current. Course formats, prices, and certification exam fees change and vary by region. We review our guides regularly, and we always recommend confirming the specifics on the provider's official page before you enrol.