CF

AI Agents Bootcamp: Zero to Mastery

6h 55m 29s
English
Paid

This is not a course about "smart prompts" – this is a course about creating real AI systems that truly get the job done.

You will go beyond simple chatbots and master structured multi-agent workflows. Using Python and modern tools for AI agents, including CrewAI, LangGraph, MCP, OpenAI, and others, you will learn how to assemble entire "teams" of artificial intelligence that automatically parse websites, analyze content, interact with each other, and report on the results.

The course is suitable for preparation for work in fields such as AI engineering, automation, product strategy, as well as for those who want to create autonomous AI systems to solve everyday tasks.

By the end of the course, you will have several ready-for-production demonstration projects and confidence that you can design, develop, and explain the operation of intelligent systems that go far beyond simple chat communication.

About the Author: Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

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#1: The AI Agents Bootcamp: Zero to Mastery
All Course Lessons (70)
#Lesson TitleDurationAccess
1
The AI Agents Bootcamp: Zero to Mastery Demo
01:49
2
Why AI Agents?
11:13
3
What We're Building (Panic! I Have a Job Interview Tomorrow!)
02:27
4
Getting an OpenAI API Key
04:31
5
CrewAI Setup
04:50
6
Get the Data
05:54
7
About CrewAI
07:51
8
AI Interviewer Agent
07:32
9
Interview Preparation Task
03:46
10
Assemble First Crew
05:05
11
Second AI Agent
07:15
12
Adding Knowledge Sources
08:14
13
Game Plan for Pro Level
04:01
14
CrewAI Setup
06:30
15
AI Research Agent
08:11
16
AI Research Tasks
05:08
17
AI Coach and Interview Tasks
06:24
18
Assembling First Crew and Exporting
09:26
19
Interviewing and Coaching Tasks
06:43
20
Second AI Crew
07:06
21
Hierarchical Process
06:34
22
Andrei Asks For Your Help!
01:37
23
CrewAI Setup
03:51
24
Research AI Agent and 1st Task
08:31
25
Research and Sentiment Tasks
09:46
26
AI Scoring Agent and Task
07:38
27
Final AI Crew
11:38
28
Game Plan for the Joke Bot
01:49
29
LangGraph Setup
03:14
30
TypedDict
04:49
31
TypedDict Class
02:39
32
Greet User
05:45
33
Get Topic of Joke - Part 1
07:34
34
Get Topic of Joke - Part 2
03:54
35
Exercise: Imposter Syndrome
02:57
36
Begin Workflow
04:46
37
Workflow with Conditional Edges
07:51
38
Invalid Topic
03:27
39
Time to Say Goodbye
09:34
40
Game Plan for Interview Coach
03:05
41
AgentState Class
02:15
42
Web Browsing with OpenAI
08:27
43
Research Company
05:59
44
Research Interviewer
05:27
45
Generate Interview Questions - Part 1
11:45
46
Generate Interview Questions - Part 2
05:46
47
Build LangGraph Workflow
07:20
48
AI Interviewer
12:28
49
Get Answer
04:39
50
End the Flow
10:26
51
Game Plan for the AI Sommelier
01:17
52
OpenAI Agend SDK Setup
03:23
53
Uploading a File
03:59
54
Vector Stores
04:01
55
AI Sommelier
06:15
56
Escalate to Boss
03:26
57
Running the AI Agent
08:27
58
Finalizing AI Sommelier
03:25
59
More Stuff That I Built
02:57
60
Introduction
12:21
61
Game Plan for MCP with OpenAI
08:11
62
Setup
03:25
63
Web Browsing with Fetch
10:22
64
MCP Approval
03:33
65
Including Approval in API Call
05:17
66
Specifying Approvals: Deepwiki - Part 1
06:50
67
Specifying Approvals: Deepwiki - Part 2
03:34
68
Authentication with Stripe MCP - Part 1
06:42
69
Authentication with Stripe MCP - Part 2
07:19
70
Thank You!
01:18
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Frequently asked questions

What are the prerequisites for enrolling in this course?
The course assumes a basic understanding of Python, as it involves using tools like CrewAI, LangGraph, and OpenAI to build AI systems. Familiarity with API usage, especially obtaining an OpenAI API Key, is also necessary. While prior experience with AI concepts is beneficial, the course is designed to guide you through assembling AI agents even if you are not an expert.
What will students build during the course?
Students will build several AI agent systems, including an AI Interviewer, a Research AI Agent, and an AI Sommelier. These projects involve setting up workflows using CrewAI and LangGraph, parsing websites, analyzing content, and generating reports. The course focuses on creating autonomous systems capable of performing complex tasks beyond basic chatbot functionalities.
Who is the target audience for this course?
This course is ideal for individuals aiming to work in AI engineering, automation, or product strategy. It's also suitable for those interested in designing autonomous AI systems for practical tasks. The course caters to both beginners and professionals who want to deepen their understanding of AI agent systems.
What specific tools and platforms are covered in the course?
The course covers tools such as CrewAI, LangGraph, and OpenAI. Students will learn to set up these tools and use them to construct AI agents. The course also includes lessons on the OpenAI API, TypedDict, and various techniques for building workflows and integrating AI systems.
What topics are not included in this course?
The course does not focus on basic chatbot design or 'smart prompt' engineering. Instead, it emphasizes building structured multi-agent workflows and autonomous systems. It also doesn't cover non-AI-related programming topics or other programming languages beyond the necessary Python for AI agent construction.
How much time should students expect to commit to this course?
While the total runtime is not specified, the course consists of 70 lessons covering various aspects of AI agent development. Students should allocate sufficient time for each lesson, including hands-on projects and exercises, to fully grasp the material and complete the demonstration projects.
How can the skills learned in this course be applied to other careers or courses?
The skills acquired in this course are valuable for careers in AI engineering, automation, and product development. Understanding how to build and deploy AI agents can be a significant advantage in tech roles that require automation and intelligent systems. The knowledge of tools like CrewAI and LangGraph can also be beneficial for other AI-related courses.