
How the course is organized: four sections that build on each other, from Playwright foundations to a full AI QA assistant. Three tips for getting the most out of it: watch in order, pause and try things, and ship one improvement to your team every week while you learn.
A true story of a release that went wrong. What manual, traditional-automation, and AI-assisted testing each do to release time, the hidden cost of the bugs that slip through, and the dirty secret of test automation: maintenance. Ends with the step-by-step roadmap for our journey.
Hands-on with Playwright, the engine that powers everything else in this course. The architecture in one picture, your first real test explained line by line, the same test seen from the browser's point of view, and the payoff when you run it.
The difference between tests that survive and tests that rot. Find elements the way a user sees them with role and label locators, work with auto-waiting instead of fighting it, and kill copy-pasted setup code with fixtures. Ends with the four-habit resilience playbook.
Flakiness is not bad luck. The five roots of flaky tests — real network calls, animations, race conditions, shared state, and time itself — and the cure that maps one to one to each. Plus retries and trace-on-retry as safety nets while you pay the flakiness debt down.
The single most valuable AI capability for an existing test suite. The healing loop step by step, the healing function explained line by line, and a heal happening in real time then the Monday-morning-broken-suite story replayed with healing in place. A human always reviews the patch.
Beat the blank page. A generation pipeline that turns user stories into runnable Playwright specs, a real generated spec for a coupon story, and the review workflow that closes the loop so coverage keeps pace with delivery while humans stay in charge of what correct means.
The most futuristic lecture in the course, and yet real teams run it in production today. How MCP wires an agent directly to the browser, why you give it a mission instead of a script, and where exploratory agents fit in your strategy alongside the scripted suite you keep.
Everything you have built only pays off inside your pipeline. Add a triage brain to CI that classifies every failure — real bug, flaky test, or environment issue and routes it automatically: page the team, quarantine and ticket, or re-run. The setup is smaller than you expect, and the metrics make the case for you.
The final project: the whole machine in one picture resilient suite, self-healing, generation, exploration, and triage working together. A week in the life of a team running it, the glue that ties it together, and the adoption staircase: the exact order to introduce each capability at your company.
We start with foundations: what broken testing actually costs teams, and Playwright from scratch architecture, your first test line by line, and how it looks from the browser's point of view. Then we make tests survive real-world change with role-based locators, auto-waiting, and fixtures, and we cure flaky tests at the root: all five causes, each with a one-to-two-line fix.
Then the heart of the course: AI agents for QA. You'll learn what an agent really is (no hype, no magic), then put agents to work on the three highest-value jobs in testing: self-healing tests that repair their own locators and open a pull request for review, generating runnable test specs directly from user stories, and agent-driven exploratory testing with Playwright MCP, where you give the agent a mission instead of a script.
Finally, we make it pay off in production: a CI/CD pipeline with an AI triage brain that classifies every failure as a real bug, a flake, or an environment issue and routes it automatically. The capstone assembles everything into a complete AI QA assistant, and you leave with the adoption staircase: the exact order to introduce each capability at your company. By the end of the course, you'll have a portfolio-worthy AI-powered QA toolkit you can use at work from day one.