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Develop AI Agents and Multi-Agent System for QA Practice

Name: Develop AI Agents and Multi-Agent System for QA Practice
Availability: InStock
Rating: 5 (10 reviews)
Author: Srinivas Madnal

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Develop AI Agents and Multi-Agent Systems for QA Practice using LangChain, LangGraph, and LLMs

 5/5

(10) Ratings

1,234 students

IT & Software

Created by Srinivas Madnal

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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

This course includes:

3 total hours on-demand video

0 articles

1 downloadable resources

32 lessons

Full lifetime access

Access on mobile and TV

Certificate of completion

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Course content

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

Post Views: 124

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.