In the last post I explained why Playwright CLI is a better fit than Playwright MCP for AI coding agents. So before we talk about workflows, debugging, or best practices, let's make the tool clear. - What is it? - How do you install it? - And how do you run one simple command against a real website? ──────────────────────────────────────── 🟢 𝐖𝐡𝐚𝐭 𝐏𝐥𝐚𝐲𝐰𝐫𝐢𝐠𝐡𝐭 𝐂𝐋𝐈 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐈𝐬 Playwright CLI is a command-line tool for controlling a browser. You run commands in the terminal, and Playwright CLI can: ➜ Open a website ➜ Click buttons ➜ Fill inputs ➜ Press keys ➜ Take screenshots ➜ Read a page snapshot It was designed for AI coding agents. But it is not only for AI. You can use it yourself from the terminal to check that the browser opens, the page loads, and the command returns useful page information. ──────────────────────────────────────── 🧠 𝐇𝐨𝐰 𝐈𝐭 𝐅𝐢𝐭𝐬 𝐖𝐢𝐭𝐡 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐬 The workflow is simple: 1. You ask the AI agent to inspect a page or debug a UI flow. 2. The agent runs Playwright CLI commands in the terminal. 3. Playwright CLI controls the browser. 4. The agent reads the result and decides what to do next. This does not replace Selenium, Cypress, or Playwright Test. It acts as a new layer on top of the testing frameworks. ──────────────────────────────────────── 🍎 𝐈𝐧𝐬𝐭𝐚𝐥𝐥 𝐏𝐥𝐚𝐲𝐰𝐫𝐢𝐠𝐡𝐭 𝐂𝐋𝐈 𝐎𝐧 𝐌𝐚𝐜 You need `Node.js` and `npm ` first. If you already have them, check in Terminal: ──────────────── > node -v ──────────────── > npm -v ──────────────── If those commands do not work, install Node.js LTS first: ──────────────── > brew install node ──────────────── Once `node` and `npm` work, install Playwright CLI: ──────────────── > npm install -g @playwright/cli@latest ──────────────── Then verify it: ──────────────── > playwright-cli --version ──────────────── You can also print the available commands: ──────────────── > playwright-cli --help ──────────────── Now go to the project where you want to use it: ──────────────── > cd your-project-folder

🧠 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹𝘀 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀. 1. A QA engineer wrote the code. 2. Read the errors. 3. Decided what to try next. That was the normal workflow for years. But now everything has changed. Starting in early 2026, AI Coding Agents can handle all of those steps, while QA engineers act as managers and agentic leads. ──────────────────────────────────────── 🟠 𝐏𝐥𝐚𝐲𝐰𝐫𝐢𝐠𝐡𝐭 𝐌𝐂𝐏 It was the first serious tool for this new AI QA workflow. It let an AI Agent look at the page, click buttons, take page snapshots, and do basic browser tasks. Main use cases for the Playwright MCP in Test Automation: - Gathering locators for the UI tests - Debugging flaky or failed tests - Read console and network logs How it works: 1. User asks an AI agent that has access to Playwright MCP to do a task. 2. The AI coding agent controls the Playwright MCP to interact with a browser. For a while, that seemed like a great option, but soon enough it was discovered that it has a few fatal issues... ──────────────────────────────────────── 🔴 𝗣𝗹𝗮𝘆𝘄𝗿𝗶𝗴𝗵𝘁 𝗠𝗖𝗣 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗼𝗽𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘁𝗲𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Here is how Playwright MCP works: 1. It loads a full page snapshot (HTML + CSS) into the AI agent’s context after each page interaction. 2. It also loads large MCP metadata that tells the agent how to use the tool. That means Playwright MCP can eat 20–30% of that memory in a single use. And once context crosses 50–60%, agents start making mistakes and losing track of earlier instructions. So technically it works, but the context overhead and cost are not great. Quick recap: the AI agent’s context is its working memory. It holds the current conversation, instructions, code, and everything else the agent needs to stay on track. ──────────────────────────────────────── 🟢 𝐏𝐥𝐚𝐲𝐰𝐫𝐢𝐠𝐡𝐭 𝐂𝐋𝐈 Playwright CLI was built to solve those problems. It gives AI agents a simple command-line utility they can call like any other terminal command: - The agent runs small commands and gets back short results. - It reads the full HTML page only when needed, not on every interaction like Playwright MCP does.

👋 Hey there! 𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐭𝐨 𝐀𝐈 & 𝐐𝐀 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫. AI is changing Software Development. And it is changing QA with it. QA Engineers who know how to use AI will: ⬩Deliver in days what used to take two weeks ⬩Do work that used to require deep expertise. With AI, basic knowledge can produce senior-level results ⬩Get instant AI feedback on tests, code, and debugging decisions The same applies to Software Developers. AI multiplies their delivery speed. QA becomes the bottleneck. That's why companies are fighting to hire QA Engineers who can match that speed. 💡 In fact, as of early 2026, many companies started adding AI coding tasks to their interview process. QA Engineers who ignore AI won't just fall behind, they risk losing their career entirely. That's not doomsaying. In 2026, tech companies laid off 55,775 people (https://www.trueup.io/layoffs). So, are those layoffs because AI is replacing people? No. AI is not replacing anyone. People who use AI are replacing people who don’t. Unlike the transition from Manual Testing to QA Automation, which took a decade, this shift is happening fast. Capable AI Coding Agents only became real in late 2025. Just a few months later, the entire tech world had changed. That's what this community is about. It's for people who see this shift and understand that right now is not just a pivotal moment for them. It's a short golden window to become one of the first truly AI-Powered QA Automation Engineers / SDETs and set yourself up for a long, safe, and extremely high-paying QA career. ──────────────────────────────────────── 𝐀𝐛𝐨𝐮𝐭 𝐌𝐞, 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐈 𝐚𝐦 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 I'm 𝐌𝐚𝐭𝐯𝐢𝐲, a Vegas-based 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐒𝐃𝐄𝐓 with 𝟏𝟎+ 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. I’ve worked across startups and large enterprises, building QA automation frameworks and testing infrastructure across pretty much all modern stacks and tools. In 2025 I introduced AI coding agents into my team's QA Automation workflows. The team adopted it. Management noticed.

Most beginners make one mistake when they start using AI coding agents for test automation. They start with UI. That is usually where the workflow gets noisy, flaky, and hard to debug. So they blame the agent. A better first step is API testing. ──────────────────────────────────────── 1. 𝐀𝐏𝐈 𝐭𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐬 𝐦𝐨𝐫𝐞 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 With API testing, you usually work with: - Auth - Request payload - Status code - Response body Those parts are easier to define. That makes API tests easier for the agent to execute and easier for you to review. UI testing has more noise: - Loading delays - Dynamic elements - Changing locators - Rendering issues - Timing problems It is just a harder place to start. ──────────────────────────────────────── 2. 𝐀𝐏𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐚𝐫𝐞 𝐞𝐚𝐬𝐢𝐞𝐫 𝐭𝐨 𝐫𝐞𝐩𝐞𝐚𝐭 Most API testing workflows follow the same pattern. - Get auth token. - Send request. - Check response. - Assert result. AI coding agents do better when the workflow is clear and repeatable. API testing gives them that. ──────────────────────────────────────── 3. 𝐈𝐭 𝐢𝐬 𝐞𝐚𝐬𝐢𝐞𝐫 𝐭𝐨 𝐯𝐞𝐫𝐢𝐟𝐲 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐰𝐫𝐢𝐭𝐞𝐬 Most API tests are smaller and easier to review than UI flows. You can quickly check: - is the endpoint correct - is the auth flow correct - is the payload correct - do the assertions make sense - is the status code handled correctly This matters a lot. If you cannot verify the output, do not trust the output. That rule matters even more with AI coding agents. ──────────────────────────────────────── If you are new to AI coding agents for QA, start with API endpoints. 1. Create a short context doc. 2. Explain the auth flow. 3. Add example requests. 4. Define what success looks like. Then let the agent work. Review the output. Re-run. Repeat. That is one of the fastest ways to build real skill with AI test automation. ──────────────────────────────────────── 📌 Want to learn how to use AI coding agents in test automation ? Checkout this live workshop.

Two Test Frameworks. Same AI Agent. Same Prompt. Same Task. 𝗔𝗱𝗱 𝗮 𝗹𝗼𝗴𝗶𝗻 𝘁𝗲𝘀𝘁. Framework 1 result: - Test passes on first run. - Credentials pulled from the fixture. - File placed in the right folder. - Naming follows the existing convention. - Page object used. No raw selectors. Framework 2 result: - Test technically runs. - Credentials hardcoded directly in the test. - New file dropped in the root directory. - Named `test_new.py.` - Raw selectors everywhere. No page object in sight. The test in Framework 1 looks like if was written by an actual engineer. The test in Framework 2 is a mess, it is kinda of working... but still a complete mess. ──────────────────────────────────────── 🧠 𝐇𝐞𝐫𝐞 𝐈𝐬 𝐖𝐡𝐲 The AI Agent does not decide what good tests look like. It reads what already exists in your repo and continues the pattern. Framework 1 had fixture files, page objects, consistent naming, and a clear folder structure. The agent read all of that. Matched against it. Wrote a test that fits right in. Framework 2 had hardcoded values, raw selectors, no structure, and copy-pasted setup code in every file. The agent read that too. It draws one conclusion: this is the standard here. And continued exactly that pattern. ──────────────────────────────────────── 📌 𝐓𝐡𝐞 𝐔𝐧𝐜𝐨𝐦𝐟𝐨𝐫𝐭𝐚𝐛𝐥𝐞 𝐓𝐫𝐮𝐭𝐡 Before you hand your repo to an agent, ask yourself one question: “Would I be comfortable showing this code to senior engineers?” If yes, start using AI coding agents. If no, fix the framework issues first, then bring in AI. Because it won’t fix bad test automation code. It will scale it. ──────────────────────────────────────── Want to learn how to use AI coding agents in test automation ? Checkout this live workshop. 👉 https://www.skool.com/qa-automation-career-hub/welcome-to-qa-automation-roadmap-lab-start-here