Develop AI Agents and Multi-Agent System for QA Practice

About this course

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 WebdriverIOIntegrate human-in-the-loop validation for quality and control

Finally, 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 MattersTraditional 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 ForQA Engineers and SDETs looking to upskill into AI automationQA 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.

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