What you'll learn:
- Understand the fundamentals of Agentic AI, how autonomous AI agents work, and their real-world applications
- Build your first AI agent from scratch using LangGraph, one of the most widely adopted agent frameworks
- Create and integrate knowledge bases to make agents more intelligent, contextual, and efficient
- Configure agent tools, actions, and workflows to enable autonomous task execution
- Develop advanced agent capabilities using LangGraph's tool-calling and state management to build real, autonomous multi-step workflows
- Test, refine, and optimize agent behavior through hands-on exercises, assignments, and practical demos
Build Real AI Agents with LangGraph | Agentic AI, RAG, Tool Calling & Multi-Agent Systems
Master Agentic AI by building real AI agents using LangGraph through hands-on projects.
Learn LangGraph, Retrieval-Augmented Generation (RAG), Tool Calling, Knowledge Retrieval, Multi-Agent Systems, AI Workflows, Prompt Engineering, and modern AI Agent development from scratch.
If you've been searching for a practical course on Agentic AI, LangGraph, AI Agents, or RAG, you're in the right place.
Modern AI is moving beyond chatbots.
Today's AI Agents can reason, retrieve knowledge, call tools, execute workflows, collaborate with other agents, and automate real business tasks.
These skills are now in demand across software engineering, QA, cloud, automation, enterprise AI, customer support, and IT operations.
This course teaches you those skills by building real projects—not by watching theory.
The primary implementation uses LangGraph, one of today's most widely adopted frameworks for building production-ready AI Agents.
Why Choose This Course?
This course teaches you how modern AI Agents actually work. You'll learn to:
Build AI Agents using LangGraph
Create RAG-powered AI applications
Connect AI Agents to Knowledge Bases
Build Tool Calling workflows
Create intelligent multi-step AI workflows
Build Multi-Agent Systems
Apply Prompt Engineering for better reasoning
Build complete hands-on projects from scratch
Every concept is explained clearly.
Every major topic includes practical demonstrations.
You won't just watch.
You'll build.
Why Agentic AI?
Large Language Models generate answers.
AI Agents perform work.
Modern Agentic AI systems can:
Reason
Plan
Use tools
Retrieve knowledge
Execute workflows
Make decisions
Collaborate with other agents
This is rapidly becoming one of the most valuable skills in AI development.
What You'll Build
Throughout this course you'll build:
AI Agents using LangGraph
RAG-powered applications
Knowledge-aware AI Agents
Tool-enabled AI workflows
Multi-step autonomous agents
End-to-end AI Agent projects
What You'll Learn
Agentic AI fundamentals
LangGraph architecture
State, Nodes & Edges
Building AI Agents from scratch
Retrieval-Augmented Generation (RAG)
Knowledge Bases
Tool Calling
AI Agent Workflows
Multi-Agent Systems
Prompt Engineering
Agent testing and optimization
Responsible AI
Real-world business use cases
AWS Bedrock Reference Included
This course originally demonstrated Agentic AI using AWS Bedrock.
The complete AWS implementation has been preserved as an Optional Reference for:
Existing students
Learners using AWS Bedrock
Anyone interested in comparing LangGraph and AWS Bedrock
The primary learning path now uses LangGraph.
Requirements
No prior AI, LangGraph, LLM, or RAG experience required
Basic Python knowledge
A computer capable of running Python
An OpenAI API account with paid API credits (pay-as-you-go). The hands-on projects use the OpenAI API. Most learners spend only a few US dollars while completing the course.
Who This Course Is For
Beginners learning Agentic AI
Python Developers
Software Engineers
AI Engineers
QA Engineers
Cloud Engineers
Data Engineers
Data Scientists
Automation Engineers
Anyone wanting practical AI Agent development skills
If you want to build modern AI Agents instead of simple chatbots, this course will take you from beginner to building production-style Agentic AI applications through practical, hands-on projects.
