Agentic AI for Beginners: Build AI Agents with LangGraph

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.