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Every QA leader has faced the same conversation. Leadership asks: "What are we getting for our automation investment?" And the honest answer is often some version of "we're faster than we used to be" without hard numbers to back it up. That gap between intuition and evidence is where automation programs get defunded. Not because they are not delivering value, but because the value was never quantified in terms finance teams understand.

If your team uses Playwright to write tests and Katalon to manage them, you've likely felt the gap. Results sitting in local HTML files. No way to compare Playwright runs against your Selenium or Appium suites. Managers asking for a unified report, and you scrambling to stitch things together manually. That gap is now closed. The Katalon True Platform integration with Playwright is now officially here, and it changes how teams with mixed automation stacks approach Playwright test management at scale.

You push a commit. The pipeline goes red. You run it again and get green. No code changed. Nothing in the environment changed. And yet, the result is different. If that sounds familiar, you're not alone. Flaky tests in test automation are one of the biggest hidden productivity drains in modern software delivery, and most teams are still treating them as a minor annoyance rather than a systemic problem. Spoiler: they're not minor. And the way teams traditionally try to fix flaky tests? It mostly backfires.

A failed payment is never just a technical failure. It is the moment a customer loses confidence in your platform, abandons their purchase, and often does not return. Unlike most bugs, a payment failure has a direct and immediate cost. This guide gives you the test cases to prevent that. Below you will find over 50 test cases organised by type and filterable by category, guidance on using AI to speed up your test suite, and a pre-launch checklist you can work through before every release.

Play Quality People - Huy Tieu: The Evolution of Agentic QA: From scripts to Harnesses 17: 18 A conversation with Huy Tieu, Senior Product Manager at Katalon, on why the scripted testing model broke, what replaces it, and the one experiment every QA leader should run this week. The term "Agentic QA" is everywhere in 2026. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by year's end, up from under 5% in 2025.

AI-assisted development means more code, faster release cycles, and a testing surface area that keeps expanding, but QA team size hasn't kept pace. The result is a pressure most QA engineers know well: you're not behind because you're slow. You're behind because the math no longer works. Most tools respond by adding AI at the margins: a suggestion feature here, a copilot shortcut there.

Software testing is going through its biggest shift since teams moved from manual to automated testing. The difference this time? The AI isn't just helping testers write scripts faster. It's making decisions about what to test, when to test it, and what to do when something breaks. This is Agentic QA. And if you're a QA leader, engineer, or anyone responsible for software quality, it's a concept you need to understand now, not in six months.