Part of AI Career Booster Path
Playwright Advanced 2026
AI writes Playwright tests in seconds. Most of them rot within a sprint. Learn the production-grade patterns that survive AI-generated code – tiered locators, agentic workflows, and network control — on real streaming apps.
Finished 0/35 lectures
Course content
Companion repo
Open in Codespaces (no local setup)
Release schedule — dates and order
Repo conventions the AI agents follow
Runnable "where this breaks" examples — all modules
1.A — What's changed since Playwright 1.49
1.C — Augment, don't replace
1.D — Course tour: 20 modules, 5 tracks + extensions, 3 anchors
1.E — "Where this breaks in production" — the recurring beat
Get the repo running, and start training your eye
2.A — TypeScript strict mode
2.B — ESLint + eslint-plugin-playwright
2.C — Husky pre-commit hooks
2.D — Playwright config
2.E — Devcontainer + Codespaces (and where this breaks)
Audit your project setup against the template
3.A — The locator resilience hierarchy
3.B — getByRole deep dive on Tubi
3.C — ARIA snapshots as the structural assertion
3.D — Edge cases: iframes and shadow DOM
3.E — Where this breaks
Module 3 code and fixtures
Tubi page objects used throughout the course
Rewrite a brittle locator so it stops rotting
4.A — What page.route actually is
4.B — Static-JSON mocking on Tubi
4.C — When mocks lie to you
4.D — Partial mocking
4.E — Route ordering & precedence
4.F — Where this breaks
The committed proofs (covers lessons 4.B through 4.F)
Module 4 code and fixtures
Mock a real test, then break the mock
5.A — What a HAR actually is
5.B — Capture-and-replay with routeFromHAR
5.C — tracing.startHar() for diagnostic capture
5.D — Trace Viewer mastery
5.E — Where this breaks + your turn
The committed proofs
Module 5 code and fixtures
Diagnose a flake with trace + HAR + cURL
6.A — Why this module uses Juice Shop, not Tubi
6.B — page.routeWebSocket()
6.C — Server-Sent Events patterns
6.D — Report-generation testing
6.E — The toast is not the assertion
6.F — Contract testing against the OpenAPI spec
6.G — Modern route precedence rules
6.H — Where this breaks + your turn
Module 6 code and fixtures
Module 6 homework — three tests, three angles
Engenious University reserves the right to change the modules' order to ensure the most efficient education process.
Description
Any AI assistant can generate a Playwright test in seconds. That was the hard part until about 2024. The hard part now is that the test it writes looks correct, passes on your machine, and quietly breaks three sprints later when someone renames a CSS class – and you have forty more just like it.
If your suite has grown faster than your confidence in it, that is not a discipline problem. It is what happens when generation gets cheap and architecture stays expensive.
Playwright Advanced 2026 teaches the architecture. Across 20 modules you build the system that AI-generated tests need in order to survive: a tiered locator strategy that degrades gracefully instead of failing, project setup that constrains what an agent can produce, full control over the network layer, and the agentic workflows – Playwright Test Agents, instruction files, a custom MCP server you write yourself — that turn an assistant from a liability into leverage.
The organizing principle is one line, and every module returns to it: cheap thing first, smart thing when the cheap thing cannot carry the load. Deterministic locators where they work. The accessibility tree with an LLM where they do not. Vision only where there is no DOM at all – video players, canvas, media surfaces.
Everything is built against real applications, not a toy demo site. Tubi is the primary anchor, a live streaming service with the messy React markup and video surfaces that make tests hard. OWASP Juice Shop runs locally in Docker as the controlled target with deliberate bugs. Expand Testing covers the iframe and shadow-DOM cases Tubi does not expose.
Every module ends the same way, with a segment called where this breaks in production. Not a summary – a specific failure mode. When mocks pass but production fails. When the self-healing agent hides a real regression. When AI-generated test data conceals the bug you were looking for. That segment is the course's signature, and it is the difference between knowing a feature exists and knowing what it costs you.
Taught by Greg Goldshteyn. New modules land on a regular release cadence, so the curriculum tracks what Playwright actually does now rather than what it did in v1.49 á where most Playwright material still stops.
What you'll learn
- Take control of the network layer. page.route , HAR replay, WebSocket interception — and the three places it silently cannot help you.
- Write locators that stop rotting. A tiered strategy — deterministic, then accessibility-tree with an LLM, then vision — and the judgement to know which tier a problem belongs to.
- Run agents without losing control of your suite. The Test Agents loop end to end, instruction files that hold agents to your conventions, and your own MCP server.
- Constrain what AI can generate. Strict TypeScript, lint rules tuned for AI anti-patterns, and pre-commit gates that stop bad generated code before it merges.
- Ship and keep it running. Sharded CI with blob reports, a live capstone refactor, performance and synthetic monitoring, component testing, and evaluating AI features in a product.
- And for every technique above: exactly where it breaks in production.
Your Instructors
Award Winning Leader in Quality Assurance
Visionary QA Leader with substantial experience in the IT industry. Worked across Salesforce, Sony, and now as part of Video Engineering and Quality Assurance at Fox Media, he leads the strategy for high-concurrency streaming environments, where a single second of latency is unacceptable.
Who this course is for
- Automation engineers whose Playwright or Selenium suite has outgrown the patterns it was started with
- QA engineers who now let AI write most of their tests and want the architecture that makes that safe
- SDETs who can write a passing test but are not yet the person who decides how the framework is shaped
- Anyone maintaining a suite that is green in CI and no longer trusted by the team
- Test leads evaluating where AI genuinely belongs in a testing stack, and where it quietly costs more than it saves
- Engineers who have finished an introductory Playwright course and found the jump to production-scale work unbridged
Course requirements
System
- Windows 10/11, macOS, or Linux
- 16 GB RAM recommended
- ~10 GB free disk for browsers, Docker images, and traces
- Stable internet connection — several modules test against live sites
Tools you install
- Node.js 22 or later — the repo pins it via
.nvmrc, sonvmis recommended - Git and a GitHub account
- Visual Studio Code, or any editor with a terminal
- Docker Desktop — required for the OWASP Juice Shop modules; a few modules skip without it
- Playwright 1.61+ — installed by
npm installfrom the repo, no separate step
AI tooling
- Claude Code is the primary AI client used throughout the course, and the repo ships
CLAUDE.mdplus.claude/agents/for it - GitHub Copilot or Cursor work for most lessons — the repo carries
.github/copilot-instructions.mdand.cursor/rules/index.mdcas well - An Anthropic API key is needed for the tier-2 and tier-3 lessons in Module 11. The tier-1 path and the offline demo run without a key, so you can complete the earlier modules with no API spend at all
Zero-install alternative
- The repo is devcontainer- and Codespaces-ready. One click opens a working environment with browsers preinstalled — expect roughly 3 minutes to provision the first time
What you should already know
- You can read and write TypeScript or JavaScript; you do not need to be fluent
- You have written at least a few automated UI tests, in any framework
- Basic terminal comfort —
git,npm, running a command and reading its output - No machine-learning background of any kind is required
Engenious Discord Community
Get help, share knowledge, and grow faster with other QA engineers.
FAQ
Yes. Playwright Advanced 2026 is part of the AI Career Booster, along with every new module as it is released.
Yes – some. This is an advanced course and it assumes you can already read and write a Playwright spec file. You do not need to be an expert, and nothing here expects you to have built a framework before. If you have never written an automated test, start with the introductory Playwright course first and come back to this one.
Yes - you have 1 week after subscribing to request a refund from us.
You need to be able to read it and write basic code in it. You do not need to be fluent. The repo runs in TypeScript strict mode throughout, and Module 2 explains every setting and why it is there — so if strict mode is new to you, that is a lecture, not a prerequisite.
No. Nothing in this course involves training or building models. You use AI tools as a tester: generating code, recovering from locator rot, evaluating output. Module 1 is explicit about where AI helps and where it helps with nothing.
A production-grade test architecture, module by module: a tiered locator system that degrades gracefully instead of failing, a project setup that constrains what AI can generate, full control of the network layer, an agentic workflow using Playwright Test Agents, your own MCP server, and a CI pipeline with sharding and merged reports. Module 16 is a live capstone where you refactor a deliberately tangled suite end to end.
Three real ones, no toy demo site.
- Tubi, a live streaming service, is the primary anchor — real React markup and real video surfaces
- OWASP Juice Shop runs locally in Docker as the controlled target, with deliberate bugs
- Expand Testing covers the iframe and shadow-DOM cases Tubi does not expose
Tests reach these through adapter classes, so if a site changes, the adapter changes and the lessons do not.
Yes. Windows, macOS, and Linux all work. Node 22 is the only hard requirement, and the repo pins it. There is no Xcode-style platform lock anywhere in this course.
Node.js 22+, Git, an editor, and Docker Desktop for the Juice Shop modules. npm install handles Playwright and the browsers. If you would rather install nothing, the repo is Codespaces-ready — one click gives you a working environment, though it takes about three minutes to provision the first time.
Partly, and here is the honest breakdown.
Most of the course runs on Claude Code, and the repo ships instruction files for GitHub Copilot and Cursor as well, so you can use whichever you already have. The tier-1 locator path and the offline demo run with no API key at all, so the early modules cost you nothing extra.
The tier-2 and tier-3 lessons in Module 11 call a model directly and need an Anthropic API key. Spend there is small but real. Module 7 is entirely about the cost of generating tests four different ways, including the numbers — so you will be able to make this decision with actual figures rather than guesses.
No. Everything runs in a browser on your own machine, or in a Codespace. 16 GB RAM is comfortable; Docker is the heaviest thing you will run.
Twenty modules. Each is roughly 28–35 minutes of video, built from short lectures of 4–7 minutes each, so you can work through it in the gaps rather than blocking out an evening. It is self-paced with no deadlines — the Booster is designed around roughly 30 minutes a day.
Yes. Everything is in the public companion repo, organised per module, with a dedicated page for every module and lesson:
https://github.com/engenious-inc/playwright-advanced-2026-students
Clone it before Module 1. Each lecture description links directly to the exact folder and files it uses.
Yes. Every module ends with a where this breaks in production segment and a "your turn" task. Most tasks ask you to apply the technique to your own codebase rather than to a sandbox, because that is where you will find out whether it holds. Module 16 is a full capstone refactor.
The segment that closes every module. Instead of a summary, it names a specific failure mode of what you just learned — when mocks pass but production fails, when the self-healing agent hides a real regression, when AI-generated test data conceals the bug you were hunting. Knowing a feature exists is cheap; knowing what it costs you is the job.
Very little. Introductory material — including most of what is on YouTube — teaches Playwright as it was around v1.49. Playwright is on 1.61, and nearly everything this course is built on arrived after that: Test Agents, stable ARIA snapshots, WebSocket routing, HAR tracing. Module 1 opens by walking that gap version by version.
Yes. There is a dedicated Discord channel for the course, and instructors answer within one working day.
No. This course is self-contained. The Maestro and XCUITest courses cover mobile and are independent of this one.
No, and we would rather say so plainly. Modules 0 through 4 are live now — orientation, the full Foundations track, and the first Network module. Further modules are released on an ongoing cadence, and your access includes every one as it lands, at no extra cost.
Module 1 is the shortest module in the course on purpose, and it earns its place. It sets up the tiered model that every later module is built on, and it ends with a two-minute demo of the full agentic loop running against a live app. If you skip it, Modules 7 through 13 will feel like a list of features instead of one argument.
Yes, a course-completion certificate from Engenious University. To be clear about what that is: it is a certificate of completion, not an industry certification. The work you can show from the capstone tends to matter more in interviews than the certificate does.
Playwright moves quickly, which is exactly why this course exists — most available material stopped at v1.49. Modules are versioned in the companion repo so the code you see in a video stays reachable even after the repo moves on, and lectures are updated when a Playwright release changes something material.