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Agentic AI with LangChain and LangGraph

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IntermediateCourse

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.

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Information Technology

Skills You Will Learn

  • Artificial Intelligence, Agentic AI Development, Python, LangChain & LangGraph Workflow Design, ReAct Agent Architecture, AI Agent

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 10 Hours

Platform

  • Coursera

Last Update

  • July 24, 2025
About 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!

Learning Objectives: 

  • Build agentic AI systems using LangChain and LangGraph to support memory, iteration, and conditional control 
  • Design and implement self-improving agents using Reflection, Reflexion, and ReAct architectures 
  • Apply agent orchestration techniques to build collaborative multi-agent systems 
  • Develop agentic RAG systems that route queries and support retrieval-enhanced reasoning 

Course Syllabus

Module 1: Introduction to LangGraph 
  • Lesson 0: Welcome
  • Lesson 1: Introduction to Agentic AI 
  • Lesson 2: LangGraph versus LangChain
  • Lesson 3: Build a LangGraph Workflow
  • Lesson 4: Module Summary and Evaluation
Module 2: Build Self-Improving Agents with LangGraph
  • Lesson 1: Build Reflection Agents 
  • Lesson 2: Advanced Self-Improvement with Reflexion Agents 
  • Lesson 3: ReAct: Integrating Reasoning and Action
  • Lesson 4: Module Summary and Evaluation 
Module 3: Multi-Agent Systems and Agentic RAG with LangGraph 
  • Lesson 1: The Evolution from Single to Multiagent Systems
  • Lesson 2: Build Multi-Agent Applications
  • Lesson 3: Module Summary and Evaluation 
  • Lesson 4: Course Wrap-Up

Prerequisites

Python programming skills, basic understanding of LangChain, and familiarity with core AI concepts 



Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

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Karan Goswami

Data Scientist at IBM

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Contributors

Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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