Build Agentic AI & Multi-Agent System for Software Testing
What you'll learn
- Understand the power of LLMs in Software Testing
- Understand how to use LangChain to interact with LLMs
- Understand how to using Local LLMs with Ollama for Building Agent tools with LangChain
- Understand building AI Agents, MultiAgents and Toolings for Software Testing
- Understand the power of AI Agents to simplify Software Testing processes
Requirements
- Basic knowledge of python or any programming language
- Basic knowledge of working with ChatGPT or simple prompts
Description
Welcome to my course Build GenAI & Multi-Agent Systems Tools for Software Testing
In this hands-on course, you’ll learn to harness the power of Generative AI, AI Agents, and Multi-Agent Systems to build real-world tools for software testing. Whether you’re a QA engineer, SDET, or developer aiming to level up your automation skills, this course equips you with practical techniques to bring AI-driven efficiency into your testing lifecycle.
Today, QA engineers are no longer limited to writing test cases and checking logs manually. With the rapid growth of LLMs (like ChatGPT, LLaMA, and Gemini) and frameworks like LangChain and AutoGen, you can now build autonomous test agents, automate log analysis, and even create collaborative multi-agent testing systems. This course gives you the tools, patterns, and hands-on skills to make that leap.
By the end of this course, you will be able to:
Understand the core concepts behind GenAI, AI Agents, and Multi-Agent Systems
Run powerful open-source LLMs locally using Ollama (no paid API needed)
Use LangChain to build intelligent tools and agents for QA automation
Create custom tools that read PDFs, parse logs, and generate test cases
Store and query data using vector stores with embeddings
Build a RAG-powered agent that analyzes logs using context retrieval
Develop a Test Case Generator Agent from product requirements
Use Playwright with agents to simulate web scraping and behavior testing
Orchestrate multi-agent collaboration using AutoGen and AutoGen Studio
Construct fully automated agents that read requirements and output test cases
Design multi-agent QA systems that mimic real QA workflows with minimal human input
Why This Course is Unique
Most AI courses focus on chatbots or language tasks. This course goes deep into the testing lifecycle and shows you how to build intelligent, context-aware agents for software quality assurance. You’ll move beyond theory and actually build working tools that:
Read your requirements
Understand logs and test results
Generate test scripts and summaries
Work together as a team of AI testers
All using open-source tools, local models, and practical Python code.
Who this course is for:
- QA
- Dev
- AI QA
- DevOps
- AI Agentic Engineers
Instructor
I am Karthik K.K. I do consulting, blogging and researching on various different tools and technologies which inspire my interest. I have been into Software Automation Testing for over 20 years now and my interest in learning new stuffs which enables automation has never been compromised.
Due to my passion in automation testing I got fully exposed to tools and languages like
AI and Machine Learning: Built an Tool for Playwright MCP Server, LangChain, DeepEval, RAGAs, HuggingFace, Fine-tuning LLMs
Desktop & Web : Cypress, Selenium, Specflow,Puppeteer,Plawright, UFT, VS Coded UI, Ranorex ,Test Complete and Cucumber
Mobile : Appium, Robotium,Calabash, Espresso, Selendroid
Languages : C#, Java, Ruby, Powershell, Javascript, VBScript
Cloud Tools : Azure, Dockers
Misc Tools: Mockito, Rest-assured, Bound-Box,FluentAutomation
Interested in: MVC, Entity framework, LINQ, Fakes, Distributed deployment, SAAS, Event-Driven System