CF

AI Agents Masterclass

24h 27m 34s
English
Paid

AI Agents Masterclass gives you a clear, practical path into agent development. You work with real tools and build agents that solve real tasks. The goal is simple: you learn how agents think, act, and work in production systems.

Why This Course Stands Out

You focus on practice from the start. You build more than ten agents, and each one solves a clear task. This helps you see how agents behave in real conditions.

Hands-on Work

You create agents for data work, search, content tasks, support tasks, and workflow steps. Each agent has code, logic, and a live demo.

Modern Frameworks

You work with tools used in current AI products:

  • CrewAI
  • AutoGen
  • OpenAI Agents SDK
  • Google ADK
  • LangGraph

You see how each tool is built and where it fits best.

Engineer Mindset

The course shows you how to think like an agent engineer. You learn how to plan agent logic and handle state and memory.

Core Skills

  • designing clear agent steps
  • tracking state and memory
  • setting up agent-to-agent talk
  • picking the right design for each task
  • scaling agents for real use

Projects You Build

You build working agents from the first lessons. Each project covers one problem and shows you how to solve it with tools and rules.

Sample Projects

  • an agent that reads and sorts news
  • a job search agent that filters roles
  • a research agent that forms clear findings
  • AI assistants and chatbots
  • a basic investment helper
  • a tool that creates YouTube Shorts and covers
  • AI tutors and consultants

Who Should Join

  • developers who want to enter agent work
  • engineers who work with LLMs or automation
  • founders and product leads building AI features
  • people who want to replace manual tasks with agents

The level is intermediate. You only need basic programming skills.

What You Will Learn

By the end, you will know how modern agents work and how to use the right tool for each job.

  • understand core agent design ideas
  • pick and use the right framework
  • plan and build agents for real tasks
  • create a small portfolio of agent projects
  • apply agents in products and automation

About the Author: Nomad Coders

Nomad Coders thumbnail

Nomad Coders is a Korean-origin online coding school founded by Nicolas Serrano (Nico). The school is one of the largest in the Korean developer-education market and has expanded internationally, publishing courses in both Korean and English that emphasise project-based learning across modern web and mobile stacks.

The CourseFlix listing carries eight Nomad Coders courses covering React, React Native, Next.js, NestJS, Twitter / Instagram clones, and the surrounding ecosystem (auth, real-time updates, deployment). Material is paid and aimed at developers who learn best from building complete applications end-to-end rather than studying frameworks in isolation.

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#1: 0.2 Welcome
All Course Lessons (151)
#Lesson TitleDurationAccess
1
0.2 Welcome Demo
01:37
2
0.3 Why So Many Frameworks
05:12
3
0.4 Course Structure
06:33
4
0.5 Requirements
01:47
5
0.6 Breaking Changes
03:41
6
1.0 UV
04:33
7
1.1 PyProject
10:30
8
1.2 Jupyter
05:21
9
2.0 Setup
03:46
10
2.1 Your First AI Response
06:18
11
2.2 Your First AI Agent
07:32
12
2.3 Adding Memory
09:01
13
2.4 Adding Tools
10:23
14
2.5 Adding Function Calling
12:03
15
2.6 Tool Results
09:01
16
2.7 Conclusions
01:21
17
3.0 Introduction
06:25
18
3.1 Your First CrewAI Agent
15:52
19
3.2 Custom Tools
09:02
20
3.3 News Reader Tasks and Agents
15:11
21
3.4 News Reader Crew
16:39
22
3.5 Conclusions
03:13
23
4.0 Introduction
05:48
24
4.1 Agents and Tasks
09:39
25
4.2 Context And Structured Outputs
13:38
26
4.3 Firecrawl Tool
13:00
27
4.4 Knowledge Sources
03:23
28
4.5 Conclusions
05:09
29
5.0 Introduction
04:51
30
5.1 Your First Flow
13:28
31
5.2 Content Pipeline Flow
11:43
32
5.3 Refinement Loop
07:34
33
5.4 LLMs and Agents
15:19
34
5.5 Adding Crews To Flows
18:09
35
5.6 Conclusions
02:10
36
5.7 Outro
02:16
37
6.0 Introducton
07:36
38
6.1 Email Optimizer Team
16:21
39
6.2 Deep Research
15:36
40
6.3 Conclusions
01:35
41
7.0 Introduction
06:32
42
7.1 Agents and Runners
14:33
43
7.2 Stream Events
12:45
44
7.3 Session Memory
09:38
45
7.4 Handoffs
05:47
46
7.5 Viz and Structured Outputs
03:08
47
7.6 Tracing
06:10
48
7.7 Conclusions
01:09
49
7.8 Welcome To Streamlit
11:51
50
7.9 Streamlit Data Flow
10:45
51
8.0 Chat UI
13:14
52
8.1 Conversation History
07:57
53
8.2 Web Search Tool
21:01
54
8.3 File Search Tool
18:11
55
8.4 Multi Modal Agent
10:16
56
8.5 Image Generation Tool
12:25
57
8.6 Code Interpreter Tool
13:16
58
8.7 Hosted MCP Tool
12:08
59
8.8 Local MCP Server
07:57
60
8.9 Conclusions
03:13
61
9.0 Introduction
07:04
62
9.1 Context Management
08:22
63
9.2 Dynamic Instructions
06:52
64
9.3 Input Guardrails
14:43
65
9.4 Handoffs
19:34
66
9.5 Handoff UI
06:30
67
9.6 Hooks
06:33
68
9.7 Output Guardrails
09:47
69
9.8 Voice Agent I
10:57
70
9.9 Voice Agent II
13:32
71
10.0 Introduction
03:35
72
10.1 ADK Web
09:17
73
10.2 Tools and Subagents
11:04
74
10.3 Agent Architecture
15:51
75
10.4 Agent State
10:26
76
10.5 Artifacts
04:48
77
11.0 Introduction
06:33
78
11.1 Content Planner Agent
16:35
79
11.2 Prompt Builder Agent
13:28
80
11.3 Image Builder Agent
15:08
81
11.4 Audio Narration Agent
16:29
82
11.5 Video Assembly
11:36
83
11.6 Callbacks
16:33
84
11.7 Conclusions
02:13
85
12.0 Introduction
03:22
86
12.1 LoopAgent
13:29
87
12.2 Agent Evaluations
17:17
88
12.3 API Server
14:07
89
12.4 Sever Sent Events
04:58
90
12.5 Invocation Flow
09:22
91
12.6 Runner
12:45
92
12.7 Deployment to VertexAI
18:14
93
13.0 Introduction
06:37
94
13.1 Your First Graph
14:28
95
13.2 Graph State
13:48
96
13.3 Recap
09:57
97
13.4 Multiple Schemas
12:51
98
13.5 Reducer Functions
11:44
99
13.6 Node Caching
03:57
100
13.7 Conditional Edges
10:37
101
13.8 Send API
13:44
102
13.9 Command
08:07
103
14.0 LangGraph Chatbot
13:13
104
14.1 Tool Nodes
11:37
105
14.2 Memory
11:09
106
14.3 Human-in-the-loop
19:01
107
14.4 Time Travel
16:11
108
14.5 DevTools
12:48
109
15.0 Introduction
03:57
110
15.1 Audio Extraction and Transcription
16:18
111
15.2 Summarizer Nodes
12:49
112
15.3 Thumbnail Sketcher Nodes
16:25
113
15.4 Human Feedback
18:56
114
15.5 HD Thumbnail Generation
08:32
115
16.0 Introduction
05:21
116
16.1 Prompt Chaining Architecture
14:09
117
16.2 Prompt Chaining Gate
03:30
118
16.3 Routing Architecture
16:35
119
16.4 Parallelization Architecture
14:24
120
16.5 Orchestrator-workers Architecture
13:14
121
16.6 Conclusions
04:04
122
17.0 Introduction
03:08
123
17.1 Email Graph
08:15
124
17.2 Pytest
07:38
125
17.3 Testing Nodes
10:30
126
17.4 AI Nodes
05:36
127
17.5 Testing AI Nodes
03:50
128
17.6 Testing AI Responses
06:56
129
18.0 Introduction
02:37
130
18.1 Network Architecture
15:54
131
18.2 Network Visualization
06:36
132
18.3 Supervisor Architecture
11:52
133
18.4 Supervisor As Tools
03:25
134
18.5 Prebuilt Agents
16:03
135
19.0 Introduction
01:02
136
19.1 Classification Agent
13:52
137
19.2 Feynman Agent
17:15
138
19.3 Quiz Agent
17:23
139
19.4 Conclusions
00:50
140
20.0 Introduction
03:38
141
20.1 A2A Using ADK
08:28
142
20.2 A2A For Dummies
09:14
143
20.3 RemoteA2aAgent
07:30
144
20.4 FastAPI Server
16:29
145
20.5 SendMessageResponse
07:14
146
21.0 Introduction
01:26
147
21.1 Conversations API
12:03
148
21.2 Sync Responses
05:56
149
21.3 StreamingResponse
12:05
150
21.4 Deployment
06:16
151
21.5 Conclusions
01:14
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Frequently asked questions

What prerequisites do I need before starting this course?
Before starting this course, you should have a basic understanding of programming concepts and some familiarity with Python, as you'll work with tools such as CrewAI and LangGraph. Experience with Jupyter and PyProject is beneficial as these are covered in early lessons (1.1 and 1.2) to set up your environment for AI agent development.
What kind of projects will I build in the course?
You will build more than ten AI agents that solve specific tasks, such as a news reader agent, a job search agent, a basic investment helper, and a tool for creating YouTube Shorts. Each project helps you understand how to apply agent logic to real-world scenarios using various tools and frameworks covered in the course.
Who is the target audience for this course?
This course is designed for individuals interested in developing AI agents. It is suitable for those who want practical experience in building and deploying AI solutions, especially if you're aiming to work as an AI engineer or in a related field where understanding agent logic and state management is crucial.
What frameworks and tools will be covered in the course?
The course covers several modern frameworks and tools used in AI, including CrewAI, AutoGen, OpenAI Agents SDK, Google ADK, and LangGraph. You will learn how each of these tools is built and where they fit best in the development and deployment of AI agents.
What topics are not covered in this course?
The course does not cover basic AI theory or introductory programming, as it focuses on practical agent development. While you learn to build functional agents, in-depth exploration of machine learning models or advanced algorithm design is not part of the curriculum.
How does this course compare to other AI development courses in terms of depth and scope?
This course emphasizes hands-on practice by building functional AI agents from early lessons. Unlike some courses that focus more on theory, this course provides practical exposure to real-world tools and frameworks, helping you understand agent behavior in production systems.
How much time should I expect to commit to successfully complete the course?
The course consists of 151 lessons, and while the total runtime is not specified, you should plan to dedicate a few hours per week over several weeks to fully engage with the lessons, projects, and tools. Consistent practice and application of the concepts learned in each lesson will be necessary to build the agents effectively.