Unlock the full power of agentic AI by learning to design intelligent, stateful agents that can reason, improve, and collaborate. This course teaches you how to use LangChain and LangGraph to build workflows that incorporate memory, iteration, conditional logic, and real agent decision-making. You’ll explore how nodes, edges, and shared states enable flexible graph-based control structures for advanced AI systems.

After completing this course, you will be able to:

Build agentic AI systems using LangChain and LangGraph to support memory, iteration, and conditional logic
Design and implement self-improving agents using Reflection, Reflexion, and ReAct architectures
Apply agent orchestration techniques to build collaborative multi-agent systems
Implement agentic RAG systems that route queries and support retrieval-enhanced reasoning
Construct workflows that incorporate dynamic state, routing, and evaluation steps
Create adaptive agents capable of refining reasoning based on feedback

Hands-on labs guide you through implementing Reflection, Reflexion, and ReAct architectures to build self-improving agents. You’ll learn how agents evaluate their own outputs, integrate feedback, adjust reasoning steps, and refine performance over time using structured prompt engineering and reflection loops.

You’ll design multi-agent systems where specialized agents coordinate tasks, share information, and solve complex problems. Using agentic RAG techniques, you’ll build workflows that route queries to the right agent, retrieve relevant context, and return well-reasoned answers.

By the end of the course, you’ll have practical experience building adaptive, collaborative agentic systems and the skills to design scalable AI architectures for real-world applications.

The following skills are required to be successful with this course:

Python programming skills
Basic understanding of LangChain
Familiarity with core AI concepts

Course Syllabus

Welcome

Course Introduction
Course Overview
RAG and Agentic AI Professional Certificate Overview
Reading: Helpful Tips for Course Completion

Module 1: Introduction to LangGraph

Module Summary and Learning Objectives
Introduction to Agentic AI
Generative versus Agentic AI
Reading: Agentic AI
Practice Quiz: Introduction to Agentic AI
LangGraph versus LangChain
Core Components of LangGraph
Reading: LangGraph Architecture: Designing Effective Agentic Workflows
LangGraph versus LangChain: When to Use What
Reading: LangGraph versus LangChain: Pros, Cons, and Use Cases
Practice Quiz: LangGraph versus LangChain
Build a LangGraph Workflow
Getting Started with LangGraph 101
Lab: LangGraph 101: Building Stateful AI Workflows
Practice Quiz: Build a LangGraph Workflow
Summary and Highlights
Cheat Sheet: Introduction to LangGraph
Graded Quiz: Introduction to LangGraph

Module 2: Build Self-Improving Agents with LangGraph

Module Summary and Learning Objectives
Build Reflection Agents
Overview: Types of AI Agents
The Art of AI Self-Improvement: Building Reflection Agents
Lab: Building a Reflection Agent with LangGraph
Practice Quiz: Build Reflection Agents
Advanced Self-Improvement with Reflexion Agents
Reading: Structuring LLM Tool Calls with Pydantic and JSON
Understanding Reflexion Agents
Building Reflexion Agents
Lab: Building a Reflexion Agent with External Knowledge
Practice Quiz: Advanced Self-Improvement with Reflexion Agents
ReAct: Integrating Reasoning and Action
ReAct: Building Agents that Reason Before Acting
Lab: ReAct: Build Reasoning and Acting AI Agents
Practice Quiz: ReAct: Integrating Reasoning and Action
Summary and Highlights
Cheat Sheet: Build Self-Improving Agents with LangGraph
Graded Quiz: Build Self-Improving Agents with LangGraph

Module 3: Multi-Agent Systems and Agentic RAG with LangGraph

Module Summary and Learning Objectives
The Evolution from Single to Multi-Agent Systems
Introduction to Multi-Agent Systems
Reading: Multi-Agent LLM System Fundamentals
Risks of Agentic AI: What You Need to Know
Practice Quiz: The Evolution from Single to Multi-Agent Systems
Build Multi-Agent Applications
Agentic RAG: Enhance Retrieval with Multi-Agent Systems
Reading: Building Multi-Agent Systems with LangGraph
Lab: DocChat: Build a Multi-Agent RAG System
Practice Quiz: Build Multi-Agent Applications
Summary and Highlights
Cheat Sheet: Multi-Agent Systems and Agentic RAG with LangGraph
Graded Quiz: Multi-Agent Systems and Agentic RAG with LangGraph

Agentic AI: LangChain and LangGraph

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