AI Agents & MCP Explained for Smarter Software Testing

Name: AI Agents & MCP Explained for Smarter Software Testing
Rating: 4.6 (27 reviews)

Build smarter testing workflows using MCP & AI Agents — Playwright, Atlassian, Chrome DevTools, and custom servers.

Created byAmbreen Khan

Last updated 10/2025

English

English [Auto],

What you'll learn

Understand the fundamentals of Model Context Protocol (MCP) and how it connects AI agents, tools, and environments for real-world software testing workflows.
Set up and use MCP servers and clients, including Playwright MCP, GitHub MCP, Jira MCP, and others, directly inside VS Code.
Build and customize AI-powered testing workflows using GitHub Copilot’s agentic capabilities, MCP tools, and real project integrations.
Perform accessibility, console, and network audits through Playwright MCP and learn to extend MCP with your own tools.

Course content

5 sections • 23 lectures • 1h 25m total length

Introduction1:28
Explore how ai agents transform software testing by executing tests through the model context protocol with mcp servers, enabling browser automation, ui tests, and automated reporting using Playwright.
What is generative AI?3:20
Explore generative AI foundations and how large language models learn from data to predict the next word, generate content, and assist testing tasks with prompts.
What is an AI Agent?2:36
Explore how AI agents act on your behalf by planning, taking actions, and interacting with tools, blending an LLM with APIs and databases for smarter, scalable testing.
4-AI Agent Vs Automation.mp43:23
Differentiate automation from ai agents in software testing by showing how an agent reasons, uses tools, and relies on brain, memory, and tools to diagnose failures.
AI Agent Vs Agentic AI3:00
Differentiate AI agents from agentic AI, where agents are specialized testers and agentic AI orchestrates multiple agents to drive end-to-end testing and continuous improvement.
Test Your Knowledge About AI Foundations

Understanding Github Copilot Agentic capabilities8:41
Explore how GitHub Copilot evolves from autocomplete to agent mode, coding agent, and CLI. See it act as a real-time editor collaborator, writing, testing, debugging, refactoring, and generating documentation.
Github Copilot Agent Mode: Using Fetch Tool to Scrape Data2:21
Showcases using GitHub Copilot agent mode to scrape product data from an ecommerce site, extracting names, prices, and URLs, and exporting outputs to markdown and JSON for testing.
Github Copilot: Custom Chat Mode2:35
Discover how to create custom chat modes in GitHub Copilot, using frontmatter and instructions to tailor testing workflows and generate test cases in CSV format.
Popular AI Agent Frameworks2:24
Learn about popular ai agent frameworks, including Lang chain, Landgraf, Crew A.I., Autogen, OpenAI's agent SDK, Google's ADK, and Agent Squad, connecting llms to data and workflows.
Test Your Knowledge About AI Agentic Mode

Why is the Model Context Protocol gaining so much attention?0:57
Discover how the model context protocol offloads defining and running tools to MCP servers, enabling AI agents to discover, understand, and invoke tools, resources, and prompts via natural language.
Setting Up Playwright MCP Server7:09
Install and configure a Playwright MCP server in VSCode to automate browser tasks via Copilot agent mode, using NCP to link AI with files, databases, and web APIs.
Using Playwright MCP Server for Website Accessibility Audit3:20
Learn to run a complete accessibility audit with the Playwright MCP server and GitHub Copilot agent, using a prompt-driven checklist to generate a markdown report with executive summary and findings.
Using Playwright MCP Server for Console Messages Audit2:14
Learn to use the Playwright MCP server with GitHub Copilot to audit console messages, analyze errors, and generate a markdown report with severity-based insights and actions.
Using Playwright MCP Server for Network Traffic Audit2:10
Explore how to use Playwright with the MCP server and GitHub Copilot to capture, analyze, and audit website network traffic, identify errors, and generate actionable optimization insights.
Using Playwright MCP Extension for Authenticated Session Access2:41
Connect to your existing browser session via the Playwright MCP Chrome extension to let the AI assistant analyze and automate Script Lab pages without extra authentication.
Using Playwright Agents: Planner, Generator & Healer3:55
leverage playwright's planner, generator, and healer to plan, generate, and repair automated tests—using the page object model—reducing manual work and improving test reliability.
Using Chrome DevTools MCP Server to Automate Browser Debugging Tasks5:09
Discover how Chrome DevTools MCP server enables AI agents to automate browser debugging, capture traces, read logs, inspect networks, and profile performance.
Using Atlassian MCP Server to connect Jira & Confluence with Github Copilot3:07
Learn how the Atlassian MCP server bridges Jira, Confluence, and compass data with your IDE through OAuth 2.1, enabling real-time search, issue creation, and subtasks from specs with GitHub Copilot.
Using MCP Toolbox for Databases to Query Databases (No SQL Needed!)2:45
Using Gemini CLI to Connect and Interact with GitHub MCP Server6:26
Connect Gemini CLI to a GitHub MCP server to access tools and push a local bug summarizer project to GitHub using slash and cp commands and a token.
Test Your Knowledge About Model Context Protocol (MCP)

MCP Explained: How the Model Context Protocol Actually Works4:07
Explore how the model context protocol (MCP) enables seamless AI integrations by linking clients and servers through a unified data layer, transport layer, JSON-RPC, and plug-and-play tools.
Hands On: Building and Testing Your First MCP Server11:37
Build and test custom MCP servers that extend existing ones, exposing tools and prompts for testing workflows, demonstrated with a JSON validator server using UV and the Python SDK.
Test Your Technical Knowledge About MCP

Requirements

Basic understanding of software testing or QA workflows (manual or automation).
Familiarity with VS Code and running simple scripts or extensions.
Some exposure to Playwright, GitHub Copilot, or test automation tools is helpful but not mandatory.
A curious mindset to explore how AI and MCP can enhance modern testing.
No prior experience with AI agents or MCP is required — everything is explained from the ground up.

Description

Note: This course contains the use of artificial intelligence to generate the voice-over.

Master AI Agents and the Model Context Protocol (MCP) to supercharge your software testing and automation workflows! This course is designed for software testers, QA engineers, and developers who want to leverage generative AI, agentic AI, and MCP to simplify complex tasks, automate repetitive processes, and gain actionable insights from your tools and data.

In Module 1: Introduction, you’ll understand why MCP is gaining massive attention in the AI and testing ecosystem. Learn what generative AI is, explore the concept of AI Agents, and discover the differences between AI Agents, agentic AI, and traditional automation.

Module 2: Agentic AI in Action dives into real-world applications. Experience GitHub Copilot’s agentic capabilities, fetch and scrape data using Copilot Agent Mode, try custom chat modes, and explore popular AI Agent frameworks to boost productivity and testing efficiency.

In Module 3: Model Context Protocol, gain hands-on experience with MCP. Learn to set up Playwright MCP for accessibility audits, network traffic tracking, console message monitoring, and authenticated session access. Use Chrome DevTools MCP for automated browser debugging, Atlassian MCP to connect Jira & Confluence with GitHub Copilot, MCP Toolbox to query databases without SQL, and Gemini CLI to interact with GitHub MCP servers.

Finally, Module 4: Hands-On With MCP teaches you how to build and test your own MCP server, define custom tools, inspect server connections, and understand how MCP enables AI clients to seamlessly communicate with external systems.

By the end of this course, you’ll be able to create intelligent, automated workflows that save time, reduce errors, and unlock the full potential of AI in your testing and development processes.

Who this course is for:

Software Testers and QA Engineers who want to move beyond traditional testing and automation and explore AI-driven testing.
Test Automation Engineers looking to integrate GitHub Copilot, Playwright, and other MCP tools into their workflow.
Developers and SDETs interested in understanding how AI agents and the Model Context Protocol (MCP) can enhance productivity.
Tech enthusiasts and learners curious about the next generation of automation using AI and agentic systems.
Anyone who wants to future-proof their testing skills by learning how MCP connects AI agents, tools, and test environments.

AI Agents & MCP Explained for Smarter Software Testing

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 14min

Agentic AI in Action4 lectures • 16min

Model Context Protocol (MCP)11 lectures • 40min

Creating and Running a Custom MCP Server2 lectures • 16min

What's Next?1 lecture • 1min

Requirements

Description

Who this course is for: