The future of QA Testing is intelligent — powered by AI agents that can think, analyze, and execute tests autonomously.

In this course, “Develop AI Agents and Multi-Agent Systems for QA Practice using LangChain, LangGraph, and LLMs,” you’ll learn how to design, build, and deploy AI-driven QA workflows from scratch.

You’ll start by mastering LangChain fundamentals, understanding prompt engineering and Retrieval-Augmented Generation (RAG) to give your agents reasoning and memory.Then, you’ll build real QA AI agents that can:

Generate BDD test cases directly from Jira stories

Execute end-to-end browser tests using WebdriverIO

Integrate human-in-the-loop validation for quality and controlFinally, you’ll create a LangGraph-based Multi-Agent System, where multiple AI agents — Requirement Analyzer, Test Case Generator, and Test Automation Agent — work together under a Supervisor Agent to orchestrate an entire QA process autonomously.

->  Why This Course Matters

Traditional automation scripts are static and repetitive. With AI agents, your QA workflow becomes dynamic, adaptive, and continuously improving — enabling faster releases, smarter test coverage, and reduced manual intervention.

-> Who Is This Course For

QA Engineers and SDETs looking to upskill into AI automation

QA Managers exploring intelligent testing workflows

Developers, Test Architects, and anyone curious about applying LLMs and LangChain in real QA systems

By the end of this course, you’ll not just use AI — you’ll be able to build AI-powered QA systems that transform how testing is done.