babymore87
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AI Playwright Testing MCP, Agents & E2E GenAI Automation
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 51m | Size: 1.12 GB
Master AI-powered Playwright testing with MCP servers, AI agents, and CLI workflows for faster E2E automation.
What you'll learn
Build self-healing Playwright scripts using Model Context Protocol (MCP) to automatically fix broken element locators in real time.
Create autonomous AI testing agents and custom CLI tools to generate, execute, and maintain TypeScript end-to-end test suites.
Integrate Generative AI strategies into Playwright to dynamic test data generation, visual inspections, and zero-flakiness assertions.
Deploy production-ready AI Playwright suites into CI/CD pipelines with strict schema validation and deterministic result reporting.
Requirements
Basic familiarity with TypeScript or JavaScript and fundamental end-to-end testing concepts (Node.js installed locally).
Description
This course contains the use of artificial intelligence.
Stop writing brittle end-to-end tests by hand and start building a reviewable AI-assisted Playwright workflow.
This is a practical course for engineers who want to use AI without surrendering test quality. You will learn how to turn requirements into test oracles, guide AI with useful context, use Playwright MCP and agents, keep a CLI alternative available, and verify every generated change with execution evidence.
Who this course is for
•QA Automation Engineers and SDETs who use Playwright or are adopting it.
•Manual testers moving into TypeScript-based web automation.
•Developers responsible for end-to-end coverage and release confidence.
•Test leads who need a governed AI-assisted testing workflow.
•Engineering teams evaluating Playwright MCP, agents, or CLI-based skills.
What you will learn
•Design AI prompts that produce constrained, testable Playwright code.
•Convert product requirements into observable test oracles.
•Review AI-generated selectors, waits, assertions, fixtures, and test data.
•Build negative-path and boundary coverage with generative AI strategies.
•Use Playwright MCP for browser-aware exploration and test creation.
•Write project instructions and reusable skills for Playwright agents.
•Use a CLI and skills workflow when MCP is unavailable or unnecessary.
•Debug failures with traces and evidence instead of guessing.
•Refactor generated tests into maintainable page and component abstractions.
•Ship a 12-test regression slice with a CI-ready execution command.
Requirements
You should know basic JavaScript or TypeScript and understand web application behavior. Prior Playwright experience is helpful but not mandatory. You need a computer capable of running Node.js, Playwright, and a supported AI client or MCP-compatible environment. Use synthetic or approved test data; do not paste confidential production data into an AI tool.
Final project
You will build an AI-assisted Playwright regression slice for a realistic web application. The project must contain 12 end-to-end tests, including three AI-assisted tests, two negative scenarios, one authenticated fixture, one reusable page or component abstraction, and a CI-ready command. You will also submit an agent brief, a prompt-and-review log, a trace-led debugging note, and a maintenance plan. Completion means the suite runs, the output has been reviewed against the checklist, and each test has a clear business-purpose statement.
Who this course is for
QA engineers, SDETs, and developers looking to eliminate test maintenance overhead using AI agents and MCP workflows in Playwright.
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