What career opportunities can this course help me unlock?

Skills in agentic AI development are highly valuable for roles such as Software Developer, Data Scientist, Machine Learning Engineer, AI Engineer, and Automation Specialist. These positions involve building intelligent systems that use language models to reason, interact with tools, and automate complex workflows. These capabilities are increasingly in demand across industries where adaptive, language-driven automation is transforming how work gets done.

Do I need machine learning experience to take this course?

No prior machine learning (ML) experience is required. If you're comfortable with Python, you're ready to go. This course focuses on building practical agentic AI systems that reflect, improve, and act. No complex ML understanding is required.

How is agentic AI development different from traditional coding or prompt engineering?

Traditional development builds static applications, and prompt engineering fine-tunes LLM responses. But agentic AI development focuses on designing autonomous, stateful systems that can evaluate their outputs, manage memory, and interact intelligently over time. You'll learn how to architect systems that think, adapt, and collaborate, using tools such as LangGraph to build workflows with cycles, conditionals, and inter-agent communication.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Agentic AI with LangChain and LangGraph

Agentic AI with LangChain and LangGraph

This course is part of multiple programs.

Instructors: Faranak Heidari

24,808 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

97 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

97 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments

Taught in English

91%

of learners achieved a positive career outcome

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This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

Ready to build intelligent AI agents that can reason, improve, and collaborate? This hands-on course gives you the skills to build agentic AI systems using LangChain and LangGraph in just 3 weeks.

You’ll design stateful workflows that support memory, iteration, and conditional logic. You’ll explore how to build self-improving agents using Reflection, Reflexion, and ReAct architectures, empowering your agents to reason about their outputs and refine them over time. Plus, you’ll work on guided labs where you’ll structure agent feedback, integrate external data, and generate context-aware responses through step-by-step reasoning. You’ll then develop collaborative multi-agent systems that coordinate tasks, retrieve relevant data, and solve complex problems using agentic RAG. Plus, you'll gain experience in agent orchestration, query routing, and governance strategies for building robust, scalable AI applications. By the end of the course, you’ll have built working prototypes of agentic systems and gained hands-on skills to design reliable, adaptable agents. Enroll today and get ready to power up your portfolio!

Module details

This module introduces LangGraph for building intelligent, stateful AI agents that support memory, iteration, and conditional logic. You’ll explore how nodes, edges, and shared state enable dynamic workflows, and how LangGraph extends LangChain for advanced control. Through foundational concepts and hands-on practice, you’ll learn to design, build, and execute workflows that reflect real-world agentic behavior

What's included

6 videos2 readings4 assignments1 app item5 plugins

6 videosTotal 37 minutes

Course Introduction3 minutes
RAG and Agentic AI Professional Certificate Overview6 minutes
Generative versus Agentic AI7 minutes
Core Components of LangGraph 4 minutes
LangGraph versus LangChain: When to Use What  10 minutes
Getting Started with LangGraph 101 7 minutes

2 readingsTotal 6 minutes

Course Overview3 minutes
Summary and Highlights 3 minutes

4 assignmentsTotal 39 minutes

Practice Quiz: Introduction to Agentic AI6 minutes
Practice Quiz: LangGraph versus LangChain6 minutes
Practice Quiz: Build a LangGraph Workflow 6 minutes
Graded Quiz: Introduction to LangGraph 21 minutes

1 app itemTotal 60 minutes

Lab: LangGraph 101: Building Stateful AI Workflows60 minutes

5 pluginsTotal 50 minutes

Reading: Helpful Tips for Course Completion5 minutes
Reading: Agentic AI 15 minutes
Reading: LangGraph Architecture: Designing Effective Workflows10 minutes
Reading: LangGraph versus LangChain: Pros, Cons, and Practical Considerations  10 minutes
Cheat Sheet: Introduction to LangGraph 10 minutes

This module focuses on building self-improving AI agents using LangGraph. You’ll explore and implement Reflection, Reflexion, and ReAct agent architectures to design workflows that evaluate and refine their own outputs. Through guided labs, you’ll gain hands-on experience creating agents that reason, integrate feedback, and improve performance using structured approaches grounded in reflection and prompt engineering.

What's included

5 videos1 reading4 assignments3 app items2 plugins

5 videosTotal 42 minutes

Overview: Types of AI Agents10 minutes
The Art of AI Self-Improvement: Building Reflection Agents 8 minutes
Understanding Reflexion Agents6 minutes
Building Reflexion Agents8 minutes
ReAct: Building Agents that Reason Before Acting  9 minutes

1 readingTotal 3 minutes

Summary and Highlights 3 minutes

4 assignmentsTotal 39 minutes

Practice Quiz: Build Reflection Agents 6 minutes
Practice Quiz: Advanced Self-Improvement with Reflexion Agents 6 minutes
Practice Quiz: ReAct: Integrating Reasoning and Action 6 minutes
Graded Quiz: Build Self-Improving Agents with LangGraph 21 minutes

3 app itemsTotal 165 minutes

Lab: Building a Reflection Agent with LangGraph45 minutes
Lab: Building a Reflexion Agent with External Knowledge Integration 30 minutes
Lab: ReAct: Build Reasoning and Acting AI Agents with LangGraph90 minutes

2 pluginsTotal 20 minutes

Reading: Structuring LLM Tool Calls with Pydantic and JSON Serialization 10 minutes
Cheat Sheet: Build Self-Improving Agents with LangGraph 10 minutes

This module focuses on designing and implementing multi-agent systems using LangGraph. You’ll explore how specialized agents can collaborate to solve complex problems through structured orchestration. Key topics include core principles of multi-agent systems, collaboration patterns, and governance considerations. Through hands-on practice, you’ll build a multi-agent RAG system that dynamically routes queries to relevant data sources, gaining practical experience in coordinating specialized agents to enhance retrieval and reasoning. 