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

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.

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 𝐛𝐮𝐢𝐥𝐭 𝐀𝐈 𝐀𝐮𝐭𝐨𝐓𝐞𝐬𝐭 𝐋𝐢𝐯𝐞 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩 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. To get there, I spent $3,000+ of my own money. Not on theory, but on practice.

If you’re a beginner QA automation engineer or SDET (or want to become one), understanding how code goes from a developer’s laptop to production is a key skill to grow beyond just “writing tests”. In this guide, I’ll walk step by step through the software release process: • What a CI/CD pipeline is • What happens during the build–deploy–test cycle • Where QA fits into each stage By the end, you’ll have a clear picture of how releases work and how this knowledge helps you design better, more reliable automated tests. ──────────────────────────────────────── 🟢 There are many ways teams release software, but most of them are just variations of two main approaches ➤ 𝐌𝐚𝐧𝐮𝐚𝐥 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: Developer writes code → someone manually builds the app → someone manually deploys it → someone manually runs tests. Slow, error-prone, and hard to repeat the same way every time. ➤ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: Developer pushes code → Jenkins/GitHub Actions automatically builds → Automatically deploys to QA → Automatically triggers your test suite. Fast. Consistent. Reliable. Companies automate these repetitive tasks so they can release software faster with fewer bugs. Understanding this pipeline is essential if you want to move beyond writing basic test scripts and be treated as a real QA Automation Engineer / SDET. ──────────────────────────────────────── 🟢 𝐓𝐇𝐄 𝐓𝐇𝐑𝐄𝐄 𝐒𝐓𝐀𝐆𝐄𝐒 𝐎𝐅 𝐒𝐎𝐅𝐓𝐖𝐀𝐑𝐄 𝐑𝐄𝐋𝐄𝐀𝐒𝐄 Every software release follows the same pattern. Tools like Jenkins, GitHub Actions, and GitLab CI/CD automate these stages so releases happen in minutes, not hours: ┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈ 𝟏. 𝐁𝐔𝐈𝐋𝐃 𝐒𝐓𝐀𝐆𝐄 📦 (𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐝𝐞 𝐈𝐧𝐭𝐨 𝐒𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐃𝐞𝐩𝐥𝐨𝐲𝐚𝐛𝐥𝐞) Raw code can't just run on a server. It needs to be built into an artifact - a packaged, executable version of your application. 𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐭𝐚𝐠𝐞: ⟩ Dependencies get installed - External libraries and frameworks your code needs (npm install, pip install, Maven dependencies) ⟩ Code gets compiled and packaged - Source code becomes executable format:

📌 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗷𝗼𝗯? 𝗥𝗼𝗹𝗲: QA Automation Engineer - Playwright/Python (Remote) 𝗖𝗼𝗺𝗽𝗮𝗻𝘆: Via CyberCoders (recruiting firm) 𝗣𝗮𝘆 𝗥𝗮𝗻𝗴𝗲: $100,000 – $150,000 + Bonus 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Remote (New York, NY based) 𝗦𝘁𝗮𝗰𝗸: Playwright, Python, API testing, E2E automation 𝗔𝗽𝗽𝗹𝘆 𝗵𝗲𝗿𝗲: [https://www.linkedin.com/jobs/view/4334981914] This is actually one of the more realistic QA Automation job postings I've seen lately. 🔗 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗼𝗻𝗲 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁: They're not playing title inflation games. They call it what it is: 𝗤𝗔 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. No "Senior Software Engineer in Test" or "Principal Quality Architect" nonsense. Just straight to the point. 📌 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘀𝘁𝗼𝗿𝘆: This company built a B2B platform for high-stakes operations. Not consumer-facing, but mission-critical stuff. The kind of software that can't afford to break. They need someone who can build solid test automation infrastructure and actually code, not just record-and-playback scripts. ⚠️ 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆'𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿: ✅ 2+ years of E2E & API test automation using 𝗣𝗹𝗮𝘆𝘄𝗿𝗶𝗴𝗵𝘁 ✅ Hands-on coding in 𝗣𝘆𝘁𝗵𝗼𝗻 (they specifically say "not just scripting") ✅ Building QA infrastructure - test environments, performance testing, security tooling ✅ Experience testing complex web applications ✅ Manual testing when automation gaps exist 𝗕𝗼𝗻𝘂𝘀 𝘀𝗸𝗶𝗹𝗹𝘀: JavaScript, C#, WebGL, performance/security/accessibility testing, telemetry tools 🟢 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗶𝗸𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁𝗶𝗻𝗴: They're honest about needing someone who can code, not just "write test scripts." They acknowledge manual testing is still part of the job, no pretending it's 100% automation. The salary range is realistic for mid-to-senior level QA automation ($100k-$150k). They list actual tools (Playwright, Python) instead of vague "automation experience." 🚩 𝗧𝗵𝗲 𝗰𝗮𝘁𝗰𝗵: It's posted by CyberCoders, a recruiting firm. That means you're not applying directly to the company. Over 100 applicants already, competition is real. "2+ years" sounds entry-level, but they want someone building QA infrastructure. That's more like 3-5 years of actual experience.