• views: 162
• date: 28 October 2025
• posted by: 0nelove

What you'll learn
Master Prompt Engineering techniques such as Chain-of-Thought reasoning, Role Assignment, and Few-Shot examples to design effective prompts for QA-AI Agents
Build intelligent QA Agents using LangChain and LLMs that can automatically generate and refine BDD test cases from Jira user stories with human-in-the-loop
Implement Retrieval-Augmented Generation (RAG) pipelines to enhance QA agents with contextual understanding using embeddings, chunking, and vector databases
Develop and orchestrate Multi-Agent QA Systems using LangGraph, where a QA Manager agent delegates tasks between a qa_agent and qa_automation_agent
Integrate AI-driven test automation by connecting LangChain tools with WebdriverIO to execute and validate end-to-end browser tests autonomously
Requirements
Basic understanding of software testing or QA processes is helpful but not mandatory
Familiarity with Python programming (beginner level) will make it easier to follow along.
Interest in AI, automation, or LLM-based systems will enhance your learning experience
Description
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 storiesExecute end-to-end browser tests using WebdriverIOIntegrate 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 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 workflowsDevelopers, Test Architects, and anyone curious about applying LLMs and LangChain in real QA systemsBy 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.
Who this course is for
QA Engineers
SDET
Dev
AI Automation Engineers
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