A failed test is usually easy to notice.
It turns red, someone checks it, finds the problem, fixes it, and moves on.
Flaky tests are different. They pass sometimes and fail sometimes, and the difficult part is figuring out why. After seeing the same test fail randomly several times, people can slowly start treating it as "just another flaky test."
That's where things become risky.
Flaky tests can come from timing issues, unstable selectors, network delays, test data, asynchronous operations, or differences between environments. Sometimes the application is working perfectly and the test is the problem. Other times, the failure is actually pointing to a real bug.
The biggest problem is that people eventually stop trusting the test results.
Imagine a CI pipeline with hundreds of tests. If a few tests are known to fail randomly, developers may simply rerun the pipeline. But what if one of those failures is actually a real issue?
This is why we started looking at flaky tests differently while working on X360aitech. The goal isn't just to make the pipeline green. It's to understand what caused the failure and give testers enough information to decide what to do next.
Playwright is great for browser automation, but the framework itself can't solve every source of flakiness. Test design, stable locators, proper waits, isolated test data, and a reliable environment are still important. In X360aitech, we look at these execution details together with AI-assisted analysis rather than treating a failed test as just a red status.
This is where AI becomes interesting. With X360aitech, execution information such as logs, screenshots, traces, and previous test results can be used to help identify patterns around failures. Instead of spending time going through everything manually, testers can get a better starting point for investigation.
Self-healing is another area we're exploring with X360aitech. If a UI change breaks a selector, AI can help identify whether the element has changed and whether a possible replacement actually makes sense.
But there's an important distinction: self-healing shouldn't mean making every failed test pass.
If a test fails three times and passes on the fourth attempt, that doesn't automatically mean the problem is solved. The important question is still: why did it fail?
That's also how we're thinking about AI-assisted testing in X360aitech. AI should help with investigation and maintenance, but it shouldn't hide failures or make changes without enough context.
For me, good test automation isn't about having the biggest test suite. It's about having tests that the team can actually trust.
I'd rather have a smaller set of stable and meaningful tests than thousands of tests that everyone ignores when they fail.
So the next time a test fails randomly, instead of immediately clicking "Rerun", maybe ask:
Why did it fail?
Is it really flaky?
Or is it telling us something important about the application?
That's the kind of problem we're trying to solve with X360aitech making automated testing not just faster, but easier to understand and maintain.
How does your team deal with flaky tests — fix them immediately, quarantine them, or just rerun them?
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