Develop AI Agents and Multi-Agent System for QA Practice
Develop AI Agents and Multi-Agent Systems for QA Practice using LangChain, LangGraph, and LLMs
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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.
Learning Objectives
🔹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
Prerequisites
🔹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
Who This Course Is For
🔹QA Engineers🔹SDET
🔹Dev
🔹AI Automation Engineers
Course Details
| Price | FREE |
| Views | 5 |
| Lectures | 32 |
| Duration | 3 hours |
| Last Update | 25-Mar-2026 |
| Release Date | 22-Jan-2026 |
| Category | IT & Software |
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This course includes:
📹 Video lectures
📄 Downloadable resources
📱 Mobile & desktop access
🎓 Certificate of completion
♾️ Lifetime access
