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GenAI & AI Agents for QA Automation | Copilot & Claude code

Name: GenAI & AI Agents for QA Automation | Copilot & Claude code
Rating: 4.5 (20442 reviews)

Masterclass: Boost Your Testing Productivity with AI Agents (Copilot, Claude, n8n, MCP & Agentic Solutions)

Bestseller

Role Play

Created byRahul Shetty Academy - 1.2 Million QA Learners

Last updated 6/2026

English

What you'll learn

Learn how to use Gen AI LLM's effectively to maximize your QA Productivity with smart prompt engineering skills
Understand how to Generate & optimize the Test code into framework standards with Gen AI Plugins such as Github copilot etc
Understand how MCP Servers work & how they can boost LLM to be specialized Automation AI Agents
Get overview of AI Powered Testing tools in current market and their capabilities for revolutionizing Test Automtion
Learn how AI Agents work and how they can be used to perform Codeless browser Automation with help of LLM's
Learn the power of AI at terminal level using Claude code with practical Selenium Framework example
Learn n8n basics & build Automation workflows powered by AI Agents
Understand how to work with offline LLM's with full privacy and customize the LLM as per your project requirements
Learn generating Test Artifacts in fly such as TestPlan, Testcases, TestData, Bug templates for given Business requirements

Course content

13 sections • 72 lectures • 11h 9m total length

Introduction to AI Terminologies & Course Agenda - 111:57
Course Agenda Continued.. - Part 24:00
Privacy & Security of AI Applications- How companies are evolving to adapt5:18

Important Note0:44
Why Prompt Engineering matters? - Mastering it is an Art.6:04
Learn prompt engineering techniques to improve AI accuracy in quality assurance, emphasizing clear questions, context and acceptance criteria, and avoiding hallucinations through zero-shot and iterative prompting.
Adding Constraints to leverage Zero shot prompting for better AI results9:19
Practice scenarios for crafting prompts better & Few shot prompting technique6:09
Improving AI Responses with Context and Constraints
Chain of thought prompting - Let AI detail us on how it is thinking6:29
Explore chain of thought prompting by asking AI to reason step by step before answering. See how explicit thinking traces verify logic and reduce hallucinations to refine prompts.

What are tokens? Why it matters when interacting with AI Models6:59
Learn how tokens are building blocks of text, how input and output tokens affect model cost, and why precise prompts and prompt engineering matter for maximizing quality within token limits.
Understand how Context Window Limit works - Tips to save tokens6:53
Explore how context window limits and token memory impact AI conversations, and learn token-saving prompt engineering—start with precise prompts, avoid reuploading documents, and use concise follow-ups.
Optimizing AI Test Generation: A Talk with Your Project Manager
Generating Test Plan for the Project business requirements using AI14:12
Generating Test Cases for the requirements using AI10:11
Generate test cases from requirements using AI to convert test plans into functional test cases for user accounts and product catalog, with test data and end-to-end automation.
Generating Test Strategy (Shift Left Testing) with the given Test cases using AI9:11
Generate Test Data combinations for the given tests using AI10:19
Generate input and output combinations for unit testing using ai, and transform them into Cucumber Gherkin end-to-end scenarios across frameworks like Selenium or Playwright.

How this section covers AI plugins in multiple languages & editors2:04
Discover how GitHub Copilot plugins generate code inside editors like VS Code for Playwright and Cypress in JavaScript, then apply the same AI plugins to Java with IntelliJ and Selenium.
Copilot - AI Buddy to help coding inside VS code editor for Playwright/Cypress19:33
Learn to use the GitHub Copilot extension in VS Code as an AI pair programmer to assist Playwright and Cypress testing, with features like code search, optimization, and test generation.
GenAI Github copilot plugin for Selenium Java Frameworks within Intellij Editor36:24

What is MCP? How this MCP help an LLM to be super powerful28:09
Build an AI agent with MCP servers that convert plain English prompts into automated tests, orchestrating browser actions, database queries, REST API calls, and data in Excel.
Resources to download0:03
Build Agent which automates web browser using Playwright/Selenium MCP Servers14:36
Debugging steps when there are failures in configuring MCP servers0:30
Justifying the Use of MCP to Your Project Manager
Resource0:48
SQL Query
Build Agent which can extract data from SQL database by framing complex queries23:40
Hands-On Practice Resources for Testing Skills0:35
Build Agent which can perform API Testing & talk to local File systems for data18:29
Build an agent that performs api testing and reads local files to source data, using rest api and filesystem mcp servers and postman collections to follow api contracts.
Build Agent which can read/write to excel file for any given scenario12:49
Learn how to build an AI agent that reads and writes to Excel files using an MCP server, enabling end-to-end automation from browser to API calls and local file systems.
Setting up Playwright MCP configuration with in VS Code13:34
On single Prompt, create Test Automation Project and build test with locators10:17
Build a Playwright test automation project with an AI agent and MCP. Create a shopping test using exact DOM locators and run in headed mode.
Github MCP Server - Magic of commiting files and Code push to Git with Prompts9:34
Use Gemini in Your Terminal for Free: Quickstart2:12
Zero‑Cost Automation: Playwright + DevTools MCP with Gemini2:34

Create Custom chatmodes (Sub Agents) with in Github Copilot Agent mode10:19
Create custom chat modes inside GitHub Copilot agent mode using configure modes to restrict capabilities by tool, enabling browser and database agents under admin-controlled permissions for safe testing.
Advantages of Multi Specialized Agents than Single Agent holding multiple roles4:42
explore the advantages of multi agents over a single agent, including reduced complexity, isolated updates, and potential parallel execution with browser and database sub agents.
What are Agentic AI Solutions? How different they are from AI Agents10:17
Take a Quiz

Introduction to Claude Code Skill System - Problem statement5:50
Download the code base used in this section0:02
Install Claude code & Claude for Chrome and get started with /init file10:58
Tip - Good to know0:14
Understand Knowledge Skills & Agent Skills - When to use with demo example7:30
Create Skill docs for EventHub Application & Understand how they are designed13:14
Avoid Context Bloat: Use Smart References for Accurate AI Responses6:06
The Magic of Agent creating Test Scenarios by reading the Project domain doc9:49
The Magic of Agent Creating Test Strategy to push tests into different layers15:45
Create Skills for Playwright best Practices and then build Agent to write Tests15:01
Demo: Agent Running Tests and Fixing Failed Tests by Referring to Domain Docs16:50
Tip - Good to know0:17
Demo : Goal oriented Agentic Solution for the Test coverage anaylsis with report11:19

What is n8n? Overview of Business Process Automation workflows8:15
Discover n8n, a no-code workflow automation platform that combines AI capabilities with node-based integrations to automate business processes, from Google Sheets data to Gmail or Telegram alerts.
How n8n revolutionized with AI Agents encapsulation - Demo overview7:29
Create n8n AI Agent to read the Google Sheet and identify the bugs in New Status9:08
Setting up Jira cloud and Create a Project for AI Agent setup6:22
Sign up for Jira cloud or server, choose a scrum template, and create a credit card banking project to practice day-to-day QA tasks in an Atlassian environment.
Plugin Jira tool to AI Agent and create e2e n8n Workflow for business usecase13:38
Learn to build an end-to-end no-code n8n workflow that connects Jira cloud with an AI agent to auto-create bugs, manage issues, and email Jira IDs via Gmail.
Building a Public Chat Interface to Interact with n8n AI Workflow via Webhook5:09
Expose a public chat URL for your n8n workflow and interact with it via a webhook, enabling chat-driven automation with Google Sheets, Jira, and email integrations.

Using Generative AI for API Testing- parsing Json responses9:37
Generating POJO classes for complex Json and generate Java methods using AI11:12
Generating API tests in Cypress & Playwright with the given API contract10:15
Generate api tests in Cypress and Playwright from the given contract, covering add, get, and delete book operations, using fixtures and custom commands to compare nested json responses.
Generating complex SQL Queries for Datbase tables using AI9:50
Learn to generate complex sql queries with ai for database validations in e-commerce. Use inner joins, group by, and order by to identify top city sales and top categories.
Check your knowledge on MCP, LLM and AI Agents

Requirements

No programming experience needed.

Description

Course last Updated -June 2026 with topic : Claude Code Skill System workflows
AI is no longer just a buzzword in software testing. It is becoming a real productivity multiplier for QA engineers, automation testers, and quality engineering teams. This course is built to help you move beyond theory and learn how to actually use AI tools, AI agents, Claude Code, GitHub Copilot, MCP servers, n8n workflows, and low-code AI testing platforms in practical testing scenarios.

We begin with the fundamentals by covering AI testing terminology, privacy and security considerations, prompt engineering, token concepts, context window limitations, and techniques to generate better AI responses. You will learn how to use AI effectively for creating test plans, test cases, test strategies, and test data combinations from business requirements.

The course then moves into hands-on implementation. You will see how GitHub Copilot can help fix code issues and speed up automation development inside real coding environments. From there, we dive deep into Model Context Protocol (MCP) and show how to build powerful automation agents that can interact with browsers, APIs, SQL databases, local files, Excel sheets, and Git workflows.

A major highlight of this course is Building Agentic AI for Quality Engineering with Claude Code. You will learn how to work with Claude Code skill systems, create domain knowledge skills, design agent skills, avoid context bloat with smart references, and build agents that can understand project documentation, generate test scenarios, design test strategies, write tests, run tests, and even help fix failed tests by referring back to domain docs.

You will also learn how sub-agents, multi-agent collaboration, and agentic AI solutions can be used to break down complex QA responsibilities into specialized roles. In addition, the course demonstrates how to build AI agents with n8n automation workflows, integrate with tools like Jira and Google Sheets, and create practical business-oriented automation flows.

The learning does not stop there. You will also explore AI-powered API testing, AI-exclusive low-code testing tools, self-healing automation concepts, and privacy-first offline LLM setups to securely handle project domain knowledge in enterprise-friendly environments.

This course is ideal for:

QA Engineers
Automation Testers
SDETs
Manual Testers moving into AI-powered QA
Engineers curious about Claude Code, Copilot, MCP, n8n, and Agentic AI for testing

If you want to understand where QA is heading and learn how to boost testing productivity with practical AI-driven workflows, this course gives you a complete roadmap with demos, examples, and modern tools that are shaping the future of quality engineering.

Who this course is for:

Software Testers
Quality Assurance Engineers
Developers
Business Analysts
Freshers
Product owners
Scrum Masters

GenAI & AI Agents for QA Automation | Copilot & Claude code

What you'll learn

Explore related topics

Course content

Introduction to AI Testing terminologies3 lectures • 21min

Prompt Engineering - Understand 3 C's - Context, Constraints, Clarity6 lectures • 29min

Understand Tokens & Generate Test Plan, Test Cases, Test Strategy using AI7 lectures • 58min

GitHub Copilot extension for Fixing Code issues - Tips & Tricks3 lectures • 58min

Build Automation AI Agents with Model Context Protocol (MCP) Servers15 lectures • 2hr 18min

Connect the dots of Sub Agents, Multi Agent collaboration, Agentic AI Solutions3 lectures • 25min

Building Agentic AI for Quality Engineering using Claude Code13 lectures • 1hr 53min

Build QA Devops Solutions (CI/CD, Docker, GitHub Actions) with AI Agents4 lectures • 35min

Build AI Agents with n8n Automation workflows - Demo examples6 lectures • 50min

Effortlessly Generate Test Automation Code for API Testing4 lectures • 41min

Requirements

Description

Who this course is for: