Course Description

In this course, you'll learn to build a LangGraph-powered multi-agent system for software development and testing. Create developer and tester agents using the Critique–Revise pattern, connect them in a reflection loop, and visualize their workflows. Through hands-on coding and real-time debugging, you'll develop and evaluate agent performance. The course wraps up with fun mini-projects like FLAMES and Tic-Tac-Toe, helping you apply your skills to real use cases and automate development workflows efficiently.

Course curriculum

  • 1
    Course Introduction
    • Course Kick off
    • Introduction to the Course
  • 2
    Project Foundations
    • Flow of the Project
    • LangGraph Refresher
    • Code Topology Cheat‑Sheet
    • Environment Setup with LLM Initialization
  • 3
    Building Role-based Agents
    • Agent Architecture: Critique - Revise Pattern Recording.mp4
    • The Developer Agent Blueprint
    • Developer Agent Design with Python REPL Tool Recording.mp4
    • Testing Our Agentic System
    • The Tester Agent Design
  • 4
    Building Multi-Agent System
    • Building the Graph
    • The Shared Graph State
    • The LangGraph Flow Definition with Conditional Loop Logic.mp4
    • Visualizing the Graph
    • First Run – Demo Loop
    • The Graph Execution
  • 5
    Mini Projects & Wrap-Up
    • The FLAMES Game
    • Hands-on: The FLAMES Game
    • A Game of Tic-Tac-Toe
    • Hands-on: The TIC-TAC-TOE game
    • Key Benefits

Who Should Enroll

  • Developers & Testers looking to automate coding and QA workflows using AI agents.

  • AI/ML Enthusiasts eager to explore agentic systems with LangGraph and LangChain.

  • Tech Leads & Architects interested in building modular, looped LLM workflows for productivity.

  • Students & Builders who enjoy hands-on learning through real-world mini-projects like games.

What you will Learn

  • Hands-on experience building multi-agent systems using LangGraph with developer and tester roles.

  • Master the Critique–Revise loop to automate iterative code improvement and testing.

  • Learn to visualize and debug agent workflows using state tracking and Mermaid diagrams.

  • Apply concepts in real-world projects like FLAMES and Tic-Tac-Toe to solidify your skills.

About Instructor

Arun Prakash Asokan - Director Data Science, Leading Pharma Corporation

With 16+ years of industry experience, I lead and deliver end-to-end AI programs across domains, blending deep tech expertise (B.Tech, M.S. in CSE) with strategic insight from ISB Hyderabad’s Advanced Management Program in Business Analytics & AI. Recognized with the "Scholar of Excellence" and "Dean’s List" awards, I also won the global Tableau Contest in 2015. Beyond building AI solutions, I’m passionate about teaching and inspiring the next generation as a guest faculty at top B-schools and engineering colleges. A frequent keynote speaker and published author on Medium and LinkedIn, I focus on AI, data science, and emerging technologies—driving impact through education, innovation, and thought leadership.
About Instructor

FAQ

  • Do I need prior experience with LangChain or LangGraph?

    No prior experience is required. The course includes a refresher on LangGraph and guides you step-by-step through building the system.

  • What programming language is used in the course?

    The course is built entirely in Python using google collab, making it accessible for most developers and ML practitioners.

  • Will I get access to the complete codebase?

    Yes, you’ll have full access to the github repository with the code files for each section clearly mapped to the course videos.

  • Are there real-world applications beyond games?

    Absolutely! The techniques you learn—like agentic critique-revise loops and automated testing—can be extended to software development, QA, and CI/CD workflows.

  • How long will the course take to complete?

    You can complete the course in under2 to 2.5 hours, including both the walkthroughs and mini-projects.