AI Agents Masterclass

Original price was: $477.29.Current price is: $34.95.

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AI Agents Masterclass by Nomad Coders is an in-depth, hands-on course dedicated to developing modern AI agents: autonomous systems capable of reasoning, interacting with each other, using tools, and performing real-world business tasks. The course is designed to guide you from understanding the principles of AI agents to creating complex multi-agent systems applicable in production.

What Makes This Course Special

Focus on Practice, Not Theory

You don’t just learn concepts — you build over 10 full-fledged AI agents, each solving a practical task: data analysis, information search, content generation, user support, workflow automation.

Industry-Relevant Frameworks

The course covers key tools used today by AI developers and startups:

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

You’ll explore their strengths, architectural differences, and real-world application scenarios.

AI Agent Engineer Mindset

Special emphasis is placed on:

  • designing agent logic;
  • managing state and memory;
  • agent-to-agent interaction;
  • choosing the right architecture for the task;
  • scaling and maintaining agents in real-world conditions.

Projects You Will Implement

Throughout the course, you will step-by-step build agents that:

  • automatically read and analyze news;
  • search for job vacancies and filter relevant offers;
  • conduct research and draw conclusions;
  • function as AI assistants and chatbots;
  • assist with investment analysis;
  • generate YouTube Shorts and cover art;
  • act as AI tutors and consultants.

Each project is a complete system with code, logic, and a working demo.

Who This Course Is For

  • Developers who want to enter the field of AI agents.
  • Engineers working with LLMs and automation.
  • Entrepreneurs and product managers building AI products.
  • Specialists who want to replace manual work with autonomous AI systems.

Level: Intermediate; basic programming skills are sufficient.

Learning Outcomes

Upon completing the course, you will:

  • Understand how modern AI agents work.
  • Know how to choose and apply the appropriate framework.
  • Be able to independently design and implement an agent for a business task.
  • Have a portfolio of real AI projects.
  • Be ready to use AI agents in startups, products, or process automation.
Table of Contents

1 Welcome
2 Why So Many Frameworks
3 Course Structure
4 Requirements
5 Breaking Changes
6 UV
7 PyProject
8 Jupyter
9 Setup
10 Your First AI Response
11 Your First AI Agent
12 Adding Memory
13 Adding Tools
14 Adding Function Calling
15 Tool Results
16 Conclusions
17 Introduction
18 Your First CrewAI Agent
19 Custom Tools
20 News Reader Tasks and Agents
21 News Reader Crew
22 Conclusions
23 Introduction
24 Agents and Tasks
25 Context And Structured Outputs
26 Firecrawl Tool
27 Knowledge Sources
28 Conclusions
29 Introduction
30 Your First Flow
31 Content Pipeline Flow
32 Refinement Loop
33 LLMs and Agents
34 Adding Crews To Flows
35 Conclusions
36 Outro
37 Introducton
38 Email Optimizer Team
39 Deep Research
40 Conclusions
41 Introduction
42 Agents and Runners
43 Stream Events
44 Session Memory
45 Handoffs
46 Viz and Structured Outputs
47 Tracing
48 Conclusions
49 Welcome To Streamlit
50 Streamlit Data Flow
51 Chat UI
52 Conversation History
53 Web Search Tool
54 File Search Tool
55 Multi Modal Agent
56 Image Generation Tool
57 Code Interpreter Tool
58 Hosted MCP Tool
59 Local MCP Server
60 Conclusions
61 Introduction
62 Context Management
63 Dynamic Instructions
64 Input Guardrails
65 Handoffs
66 Handoff UI
67 Hooks
68 Output Guardrails
69 Voice Agent I
70 Voice Agent II
71 Introduction
72 ADK Web
73 Tools and Subagents
74 Agent Architecture
75 Agent State
76 Artifacts
77 Introduction
78 Content Planner Agent
79 Prompt Builder Agent
80 Image Builder Agent
81 Audio Narration Agent
82 Video Assembly
83 Callbacks
84 Conclusions
85 Introduction
86 LoopAgent
87 Agent Evaluations
88 API Server
89 Sever Sent Events
90 Invocation Flow
91 Runner
92 Deployment to VertexAI
93 Introduction
94 Your First Graph
95 Graph State
96 Recap
97 Multiple Schemas
98 Reducer Functions
99 Node Caching
100 Conditional Edges
101 Send API
102 Command
103 LangGraph Chatbot
104 Tool Nodes
105 Memory
106 Human-in-the-loop
107 Time Travel
108 DevTools
109 Introduction
110 Audio Extraction and Transcription
111 Summarizer Nodes
112 Thumbnail Sketcher Nodes
113 Human Feedback
114 HD Thumbnail Generation
115 Introduction
116 Prompt Chaining Architecture
117 Prompt Chaining Gate
118 Routing Architecture
119 Parallelization Architecture
120 Orchestrator-workers Architecture
121 Conclusions
122 Introduction
123 Email Graph
124 Pytest
125 Testing Nodes
126 AI Nodes
127 Testing AI Nodes
128 Testing AI Responses
129 Introduction
130 Network Architecture
131 Network Visualization
132 Supervisor Architecture
133 Supervisor As Tools
134 Prebuilt Agents
135 Introduction
136 Classification Agent
137 Feynman Agent
138 Quiz Agent
139 Conclusions
140 Introduction
141 A2A Using ADK
142 A2A For Dummies
143 RemoteA2aAgent
144 FastAPI Server
145 SendMessageResponse
146 Introduction
147 Conversations API
148 Sync Responses
149 StreamingResponse
150 Deployment
151 Conclusions

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