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Learn Agentic AI – Build Multi-Agent Automation Workflows
Section 1: Essentials of LLMs, AI Agents & MCP Servers – Build Conceptual Clarity
Lecture 1: FAQ's (Must watch)- What will you learn from this course? (7:42)
Lecture 2: What is LLM? Its Characteristic features and limitations (7:39)
Lecture 3: Transition from LLM -> AI Agents. Where AI Agent shines? (9:37)
Section 2: Getting Deep Diver into MCP Server to build an AI Agent with claude
Lecture 4: Important Note
Lecture 5: What is MCP? How this MCP help an LLM to be super powerful (28:09)
Lecture 6: Resources to download
Lecutre 7: Build Agent which automates web browser using Playwright/Selenium MCP Servers (14:36)
Lecture 8: Debugging steps when there are failures in configuring MCP servers
Lecture 9: Build Agent which can extract data from SQL database by framing complex queries (23:40)
Lecture 10: Build Agent which can perform API Testing & talk to local File systems for data (18:29)
Lecture 11: Build Agent which can read/write to excel file for any given scenario (12:49)
Section 3: Intro to Agentic AI & Setup Python IDE environment for AutoGen Framework
Lecture 12: Disadvantages of having Single Agent - and how Multi Agent fixes the problem (5:48)
Lecture 13: What is Agentic AI? Importance of Multi Agent work flows (8:36)
Lecture 14: Setting up Python and IDE to get started in Windows Machine (6:16)
Lecture 15: Setting up Python and IDE to get started in MAC Machine (6:42)
Lecture 16: Setup Python virtual enviroment & install AutoGen packages into Python Env (9:54)
Section 4: AutoGen Framework fundamentals - AssitantAgents, RoundRobinGroupchat Concepts
Lecture 17: How to kickstart running any python code and its function call (4:51)
Lecture 18: Introduction to AssistantAgent and its implementation with an example (12:53)
Lecture 19: How to make AssitantAgent answer the multimodal inputs such as images, files (14:39)
Lecture 20: What is RoundRobinGroupChat? How to coordinate between agents in a team (11:46)
Lecture 21: Full course code download
Section 5: AutoGen Framework fundamentals - Termination, StateSaving, HumanInLoop concepts
Lecture 22: What is Termination Condition and why do we need it? -MessageMax termination (8:13)
Lecture 23: How to get human in loop - Intro to UserProxyAgent class in AutoGen (17:18)
Lecture 24: State Saving Mechanism - How to switch between agents preserving state (14:06)
Lecture 25: SelectorGroupChat - How to dynamically choose which agent to act in teams (16:06)
Section 6: AutoGen Framework fundamentals - SelectorGroupChat, mcpworkbench & BrowserAgent
Lecture 26: Special Agent of Autogen which has inbuilt browser Automation capabilities (12:36)
Lecture 27: How to add MCP tooling support to Assistant Agent with mcpworkbench class (16:34)
Lecture 28: End to End example includes AssitantAgent, MCP, HumanInLoop, Teams, Termination (9:39)
Section 7: Understand the Goal of the Agentic workflow & setup Agents Prerequisites -Jira
Lecture 29: Understand the Goals of the Multi Agent workflow with a plan of execution (6:30)
Lecture 30: Setup Cloud Jira account for Agenti AI project execution (6:22)
Lecture 31: High level overview on how to create bugs and retrieve with in Jira project (7:17)
Lecture 32: Understand how to add Jira Environment variables into Assistant Agent - demo (12:31)
Section 8: Use Jira Agent & Playwright Agent to Build Autonomous Multi Agentic Workflow
Lecture 33: Create Jira Agent with Integrating Jira mcpworkbench into Assistant Agent (10:47)
Lecture 34: Create Browser Automation Agent with Integrating Playwright mcpworkbench (5:41)
Lecture 35: Important : Setting up context to Jira & Browser Agents for achieving the goal (15:49)
Lecture 36: Context - System messages of Jira
Lecture 37: Context - System messages of Browser Agent
Lecture 38: Complete end to end workflow to build Agentic AI solution with RoundRobin Teams (9:00)
Lecture 39: Run the Multi Agentic workflow & analyze the agents output behaviour in detail (11:50)
Section 9: Agent Factory Pattern-Design Multi Agent system with Database, API, Excel Agents
Lecture 40: Database-API-Excel -Understand the Goals of the Multi Agents and its workflow (6:32)
Lecture 41: What is AgentFactory? How to isolate and create Agents within factory (12:18)
Lecture 42: Part 1 - Build mcp Config file & connect Factory ,Config file to main test flow (7:28)
Lecture 43: Part 2 - Build mcp Config file & connect Factory ,Config file to main test flow (9:07)
Lecture 44: Complete end to end workflow to build Agentic AI solution with AutoGen concepts (5:56)
Section 10: Context Engineering - Define System messages to Agents for internal coordination
Lecture 45: Part 1 -Provide System messages to Database, API, Excel Agents in logical manner (26:52)
Lecture 46: Run the Multi Agentic workflow & analyze the agents output behaviour in detail (12:40)
Section 11: Summary - Recap - what did we learn? How this skill help in my next career leap?
Lecture 47: Recap of Agenti AI - Multi Agent workflow concepts learned in the course (4:48)
Lecture 48: Code download
Lecture 49: Resume inputs what you can add with the skills gained from this course
Lecture 50: Part 2-Provide System messages to Database, API, Excel Agents in logical manner (14:28)
Section 12: Glossary - Learn Python Fundamentals
Lecture 51: Python hello world Program with Basics (8:35)
Lecture 52: Datatypes in python and how to get the Type at run time (5:17)
Lecture 53: List Datatype and its operations to manipulate' (12:47)
Lecture 54: Tuple and Dictionary Data types in Python with examples (8:28)
Lecture 55: If else condition in python with working examples (3:10)
Lecture 56: How to Create Dictionaries at run time and add data into it (7:55)
Lecture 57: How loops work in Python and importance of code idendation (8:58)
Lecture 58: Programming examples using for loop - 1 (4:17)
Lecture 59: Programming examples using While loop - 2 (10:28)
Lecture 60: What are functions? How to use them in Python (10:46)
Lecture 61: OOPS Principles : Classes and objects in Python (7:38)
Lecture 62: What is Constructor and its role in Object oriented programming (13:38)
Lecture 63: Inheritance concepts with examples in Python (12:12)
Lecture 64: Strings and its functions in python (9:53)
Lecture 65: Bonus Lecture
Lecture 3: Transition from LLM -> AI Agents. Where AI Agent shines?
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