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Playwright test suites can remain unchanged while reporting results alongside Katalon Studio and manual testing activities in a centralized platform. This was the focus of the webinar Bring Playwright into Katalon True, delivered in two sessions on October 14, 2026. The webinar focused on how Playwright integrates into a broader testing workflow rather than comparing Playwright with Katalon. Playwright is a free, code-first framework for browser automation that many teams already use.

AI isn't coming for the manual tester's job. It's coming for the worst parts of it. What's left is the part you love, the part that always mattered most. And it's about to matter a lot more. There's a lot of talk right now about AI agents that can run the tests teams already have, keep them working as the application changes, and do it at a scale no team could staff. If you're a manual tester, I can guess what goes through your head when you read that. Great for the company. Where does it leave me?

Data-driven testing turns one test case into many: one script, one data file, one iteration per row. Katalon AI Assistant already writes your test cases and test data in minutes. Binding them together, though, was still a manual job that could take far longer. In Katalon Studio 11.6, the assistant does that part too.

I'll give you the takeaway from this blog right now: the part of a test worth keeping is what it's meant to prove, not the script that proves it. Scripts have become cheap to generate, so we should stop treating them as something precious to maintain. Keep the intent. Throw away the script. Last time I wrote that the first time an AI agent runs a manual test case, it can leave an automated test behind, and every later run can use that script instead of the natural-language steps.

AI is no longer on the horizon. It is already reshaping how software is built, tested, and released, and it is moving faster than most quality processes were designed to absorb. Developers are shipping AI-written code at speed. Test teams are generating test cases, maintaining suites, and triaging defects with AI. But speed cuts both ways. AI magnifies the process it enters. Strong quality practices can become faster and more scalable.

Here's the idea I want to leave you with. Your manual test library isn't documentation. It's automation that has been waiting for something that can read it. And you shouldn't have to move it, rewrite it or migrate it to set it free. The clearest version of this came up in a customer conversation a few weeks ago. The head of quality at a fast-growing fintech told me his company pays an outside firm to run its manual regression, and the firm bills per test executed.

Watch an AI agent run a regression test sometime. Really watch it. It lands on a login page. It reads the entire page structure: every element, every attribute, every nested div someone added in 2019 that nobody has dared to remove since. Then it reasons. It weighs its options. It explains to itself, at some length, that the button labeled "Sign in" is probably the one that signs you in. And then, finally, it clicks. Now multiply that by every step, in every test, in every regression run, every night.

Your manual test library is more than documentation. It contains years of decisions about what your product should do, and how to tell when it doesn't. That knowledge is a valuable starting point for automation, not something you should have to recreate from scratch. The clearest example came up in a customer conversation. The head of quality at a fast-growing fintech told me his company pays an outside firm to run its manual regression, with a bill for every test executed.

In one release test, my AI agent triaged 43 backlog items into 134 executable cases in Katalon True Platform. The first pass at the steps caused trouble. We had bundled setup, navigation, action, and judgment into long instructions. The runner returned verdicts, but a failed step left me unsure which expectation it had checked. We split the cases into one action and one checkable expected result per step. The failures became specific enough to review against the screenshots and session recording.

“Customers can apply a discount code” looks ready for testing. Give that sentence to an AI model and it can produce cases for valid, invalid, expired, and reused codes in seconds. The trouble starts when someone asks what a valid code should do. Which basket is eligible? Does the discount change the displayed total before or after tax? Should the saved order carry the same amount?