English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 129 lectures (16h 49m) | 14.04 GB
Master AI Agents in 30 days: build 8 real-world projects with OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP.
2025 is the year that Agents enter the workforce. This is nothing short of a watershed moment for Artificial Intelligence. It has never been more important to be an expert with Agentic AI. And that is precisely the goal of this course: to equip you with the skills and expertise to design, build and deploy Autonomous AI Agents, opening up new career and commercial opportunities.
This is an intensive 6-week program to master Agentic AI. We start by building foundational expertise, connecting LLMs using proven design patterns. Then, each week, we upskill with new frameworks: OpenAI Agents SDK, CrewAI, LangGraph and Autogen. The course culminates with a full week on the remarkable opportunities opened up by MCP.
Above all, this is a hands-on course. I’m a big believer that the best way to learn is by DOING. So please prepare to roll up your sleeves! We’ll build 8 real-world projects; some are astonishing, some are intriguing, and some are quite surreal. But one thing’s for sure: all are powerful demonstrations of Agentic AI’s potential to utterly transform the business landscape.
So come join me on this comprehensive 6-week journey. By the end, you will have mastered Agentic AI. You will have expertise in all the major frameworks. You’ll be well-versed in the strengths and traps of Agentic AI. You’ll confidently unleash Autonomous Agents to solve real-world commercial problems. And along the way, you’ll have had a whole lot of fun with the astounding, groundbreaking technology that is Agentic AI.
What you’ll learn
- Project 1: Career Digital Twin. Build and deploy your own Agent to represent you to potential future employers.
- Project 2: SDR Agent. An instant business application: create Sales Representatives that craft and send professional emails .
- Project 3: Deep Research. Make your own version of the essential Agentic use case: a team of Agents that carry out extensive research on any topic you choose.
- Project 4: Build a Stock Picker Agent in minutes with CrewAI—automate your search for investment gems!
- Project 5: Deploy your own 4-Agent Engineering Team—manage, build, and test software apps with CrewAI and Coder Agents in Docker!
- Project 6: Build your own version of OpenAI’s Operator Agent—your Sidekick works with you inside your browser via LangGraph!
- Project 7: Agent Creator—an Agent that builds and launches new Agents using AutoGen, unlocking endless AI possibilities!
- Project 8: Capstone—build a Trading Floor with 4 Agents making autonomous trades, powered by 6 MCP servers and 44 tools!
Who this course is for:
- Well, perhaps I’m biased, but I’d say: anyone and everyone! If you’re fascinated in the potential of Agents and hungry to have the skills to create powerful Agentic AI – then you’ve come to the right place. While it’s most suited to those with programming experience, I’ve designed the course to work for all backgrounds.
Table of Contents
Week 1
1 Day 1 – Autonomous AI Agent Demo – Using N8n to Control Smart Home Devices
2 Day 1 – AI Agent Frameworks Explained – OpenAI SDK, Crew AI, LangGraph & AutoGen
3 Day 1 – Agent Engineering Setup – Understanding Cursor IDE, UV & API Options
4 Day 1 – Windows Setup for AI Development – Git, Cursor IDE & UV Package Manager
5 Day 1 – Setting Up Your Mac for AI Projects – GitHub, Cursor IDE & OpenAI API Key
6 Day 1 – Building Your First Agentic AI Workflow with OpenAI API – Step-by-Step
7 Day 1 – Introduction to Agentic AI – Creating Multi-Step LLM Workflows + Autonomy
8 Day 2 – Building Effective Agents – LLM Autonomy & Tool Integration Explained
9 Day 2 – 5 Essential LLM Workflow Design Patterns for Building Robust AI Systems
10 Day 2 – Understanding Agent vs Workflow Patterns in LLM Application Design
11 Day 3 – Orchestrating Multiple LLMs – Comparing GPT-4o, Claude, Gemini & DeepSeek
12 Day 3 – Multi-LLM API Integration – Comparing OpenAI, Anthropic & Other Models
13 Day 3 – Comparing LLM APIs – Using OpenAI Client Library with Claude, Gemini & ++
14 Day 3 – Multi-Model Orchestration – Creating a System to Evaluate AI Responses
15 Day 3 – Connecting Agentic Patterns to Tool Use – Essential AI Building Blocks
16 Day 4 – Comparing AI Agent Frameworks – Simplicity vs Power in LLM Orchestration
17 Day 4 – Resources vs. Tools – Two Ways to Enhance LLM Capabilities in Agentic AI
18 Day 4 – Build a Web Chatbot That Acts Like You Using Gradio & OpenAI
19 Day 4 – Using Gemini to Evaluate GPT-4 Responses – A Multi-LLM Pipeline
20 Day 4 – Building Agentic LLM Workflows – Resources, Tools & Structured Outputs
21 Day 5 – Building Your Career Alter Ego – LLM Function Calling with Push Alerts
22 Day 5 – LLM Tool Calls Demystified – How to Process and Execute Function Requests
23 Day 5- Building AI Assistants – Implementing Tools for Handling Unknown Questions
24 Day 5 – Creating & Deploying an AI Agent – From Chat Loop to HuggingFace Spaces
25 Day 5 – Deploying Career Conversation Chatbots to Gradio
26 Day 5 – Foundation Week Wrap-up – Building Complete AI Agents with APIs & Tools
Week 2
27 Day 1 – Understanding Async Python – The Foundation for OpenAI Agents SDK
28 Day 1 – OpenAI Agents SDK Fundamentals – Creating, Tracing, and Running Agents
29 Day 1 – Introduction to Agent, Runner, and Trace Classes in OpenAI Agents SDK
30 Day 1 – Vibe Coding – 5 Essential Tips for Efficient Code Generation with LLMs
31 Day 1 – OpenAI Agents SDK – Understanding Core Concepts for AI Development
32 Day 2 – Build AI Sales Agents with SendGrid – Tools & Collaboration in Agent SDK
33 Day 2 – Concurrent LLM Calls – Implementing Asyncio for Parallel Agent Execution
34 Day 2 – Converting Agents into Tools – Building Hierarchical AI Systems
35 Day 2 – Agent Control Flow – When to Use Handoffs vs. Agents as Tools
36 Day 2 – From Function Calls to Agent Autonomy – Sales Automation with OpenAI SDK
37 Day 2 – Agentic AI for Business – Creating Interactive Sales Outreach Tools
38 Day 3- Multi-Model Integration – Using Gemini, DeepSeek & Grok with OpenAI Agents
39 Day 3 – Implementing Guardrails & Structured Outputs for Robust AI Agent Systems
40 Day 3- AI Safety in Practice – Implementing Guardrails for LLM Agent Applications
41 Day 4 – Building Deep Research Agents – Implementing OpenAI’s Web Search Tool
42 Day 4 – Building a Planner Agent – Using Structured Outputs with Pydantic in AI
43 Day 4 – Building an End-to-End Research Pipeline with GPT-4 Agents & Async Tasks
44 Day 4 – Building a Deep Research Agent – Parallel Searches with AsyncIO
45 Day 5 – Building a Modular AI Research System with Gradio UI Implementation
46 Day 5 – Deep Research App – Gradio to Visualize & Monitor Autonomous AI Agents
47 Day 5 – Deploying Smart Research Agents with Gradio and HuggingFace Spaces
Week 3
48 Day 1 – Crew AI Framework – Creating Collaborative AI Agent Teams
49 Day 1 – Crew AI Framework Explained – Agents, Tasks & Processing Modes Tutorial
50 Day 1 – Crew AI & LightLLM – Flexible Framework for Integrating Multiple LLMs
51 Day 1 – Crew AI Tutorial – Setting Up a Debate Project with GPT-4o mini
52 Day 1 – How to Create an AI Debate System Using Crew AI and Multiple LLMs
53 Day 1 – Building AI Debate Systems with CrewAI – Compare Different LLMs
54 Day 2 – Building Crew AI Projects – Tools, Context & Google Search Integration
55 Day 2 – Building Multi-Agent Financial Research Systems with Crew.ai
56 Day 2- Enhancing AI Agents with Web Search – Solving the Knowledge Cutoff Problem
57 Day 3 – Building a Crew AI Stock Picker – Multi-Agent System for Investments
58 Day 3 – Implementing Pydantic Outputs in Crew AI – Stock Picker Agent Tutorial
59 Day 3 – Custom Tool Development for Crew AI – JSON Schema & Push Notifications
60 Day 4 – Crew AI Memory – Vector Storage & SQL Implementation for AI Agents
61 Day 4 – Crew AI for Coding Tasks – Agents That Generate & Run Python Code
62 Day 4 – Create a Python-Writing AI Agent – Practical Implementation with Crew AI
63 Day 5 – Building AI Teams – Configure Crew AI for Collaborative Development
64 Day 5 – Collaborative AI Agent Development for a Stock Trading Framework
65 Day 5 – Building a Trading Application Using GPT-4o & Claude
66 Day 5 – From Single Modules to Complete Systems – Advanced CrewAI Techniques
Week 4
67 Day 1 – LangGraph Explained – Graph-Based Architecture for Robust AI Agents
68 Day 1 – LangGraph Explained – Framework, Studio, and Platform Components Compared
69 Day 1 – LangGraph Theory – Core Components for Building Advanced Agent Systems
70 Day 2 – LangGraph Deep Dive – Managing State in Graph-Based Agent Workflows
71 Day 2 – Mastering LangGraph – How to Define State Objects & Use Reducers
72 Day 2 – LangGraph Fundamentals – Creating Nodes, Edges & Workflows Step-by-Step
73 Day 2 – LangGraph Tutorial – Building an OpenAI Chatbot with Graph Structures
74 Day 3 – LangGraph Advanced Tutorial – Super Steps & Checkpointing Explained
75 Day 3 – Setting Up Langsmith & Creating Custom Tools for LangGraph Applications
76 Day 3 – LangGraph Tool Calling – Working with Conditional Edges & Tool Nodes
77 Day 3 – LangGraph Checkpointing – How to Maintain Memory Between Conversations
78 Day 3 – Building Persistent AI Memory with SQLite – LangGraph State Management
79 Day 4 – Playwright Integration with LangGraph – Creating Web-Browsing AI Agents
80 Day 4 – Create AI Web Assistants – Playwright, LangChain & Gradio Implementation
81 Day 4 – LLM Evaluator Agents – Creating Feedback Loops with Structured Outputs
82 Day 4- Creating LLM Feedback Loops – Worker-Evaluator Implementation in LangGraph
83 Day 4 – Building an AI Sidekick Using LangGraph, Gradio & Browser Automation
84 Day 5 – Agentic AI – Add Web Search, File System & Python REPL to Your Assistant
85 Day 5 – LangChain Tool Integration – Building a Powerful AI Sidekick from Scratch
86 Day 5 – Creating AI Workflows – Graph Builders & Node Communication Techniques
87 Day 5 – Creating Isolated User Sessions in Gradio Apps Using State Management
88 Day 5 – Inside AI Feedback Loops – Seeing How AI Evaluates & Corrects Errors
89 Day 5 – AI Assistant Upgrades – Memory, Clarifying Questions & Custom Tools
Week 5
90 Day 1 – Microsoft Autogen 0.5.1 – AI Agent Framework Explained for Beginners
91 Day 1 – AutoGen vs Other Agent Frameworks – Features & Components Compared
92 Day 1 – AutoGen Agent Chat Tutorial – Creating Tools and Database Integration
93 Day 1 – Essential AI Components – Models, Messages & Agents Explained
94 Day 2 – Advanced Autogen Agent Chat – Multimodal Features & Structured Outputs
95 Day 2 – Implementing Primary and Evaluator Agents in AutoGen with Langchain
96 Day 2 – Headless Web Scraping Tutorial – MCP Server Fetch Integration in AutoGen
97 Day 3 – AutoGen Core – The Backbone of Distributed Agent Communications
98 Day 3 – Agent Communication in Autogen Core – Message Handlers & Dispatching
99 Day 3 – AutoGenCore Agent Registration and Message Handling – Practical Examples
100 Day 3 – AutoGenCore Standalone Agents – Rock Paper Scissors with GPT-4o & Llama
101 Day 4 – Autogen Core Distributed Runtime – Architecture & Components Explained
102 Day 4 – Implementing Distributed AI Agents with AutoGen Core and gRPC Runtime
103 Day 4 – Building Distributed Agent Systems – AutoGen Cross-Process Communication
104 Day 5 – Creating Autonomous Agents That Write & Deploy Other Agents in AutoGen
105 Day 5 – Implementing Agent-to-Agent Messaging with Autogen Core & Templates
106 Day 5 – Creating Autonomous AI Agents that Collaborate Using Async Python
Week 6 – MCP
107 Day 1 – Intro to MCP – The USB-C of Agentic AI
108 Day 1 – Understanding MCP Hosts, Clients, and Servers
109 Day 1 – Using MCP Servers with OpenAI Agents SDK
110 Day 1 – Exploring Node-Based MCP Servers & Tool Access
111 Day 1 – Building an Agent That Uses Multiple MCP Servers
112 Day 1 – MCP Marketplaces & Security Considerations
113 Day 2 – Intro to Week 6 Day 2 – Building Your Own MCP Server
114 Day 2 – Wiring Business Logic into Your MCP Server
115 Day 2 – Creating Client Code to Use Your MCP Server
116 Day 2 – Wrap-Up – Capabilities of Your Custom MCP Server
117 Day 3 – Exploring Types of MCP Servers and Agent Memory
118 Day 3 – Brave Search API – MCP Server Calling the Web
119 Day 3 – Integrating Polygon API for Stock Market Data
120 Day 3 – Advanced Market Tools Using Paid Polygon Plan
121 Day 4 – What’s Next – Launching Our Agent Trading Floor
122 Day 4 – Viewing the User Interface for Trading Activity
123 Day 4 – How Trading Agents Operate and Make Decisions
124 Day 4 – Portfolio Management with Four Autonomous Agents
125 Day 5 – Which Agent Framework Should You Pick
126 Day 5 – Key Settings and Launching the Trading System
127 Day 5 – Advice for Selecting Agentic Frameworks
128 Day 5 – 10 Essential Lessons for Building Agent Solutions
129 Day 5 – Course Recap and Final Goodbye – Keep Building!
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