Systems | Development | Analytics | API | Testing

The quality tax isn't primarily a resourcing problem. It's a paradigm problem. And paradigm problems don't yield to incremental solutions.

Katalon is introducing a new MSP Program designed for our official solution and service partners. Built for partners delivering QA services across multiple customer engagements, the True Platform MSP Program offers a more flexible way to scale delivery with Katalon’s all-in-one testing platform.

In 2026, APIs have moved far beyond simple integration points. They’re now strategic business assets powering AI transformation, microservices architectures, and multi-cloud ecosystems. But a critical challenge threatens to undermine digital initiatives: the fragmentation of API testing. As organizations rush to deliver faster, they’re discovering that their testing infrastructure – cobbled together from disparate tools and disconnected processes – has become the bottleneck.

With the rise of AI use in vehicle software development, concerns arise around its presence in safety-critical applications, especially when it comes to functional safety and regulatory compliance. ISO 26262, the essential standard for automotive development that requires processes for managing, designing, and verifying safety-critical systems, still applies. However, it can fall short when applied to AI models, which are inherently non-deterministic and continuously evolving.

API testing strategies directly impact your release cycle. With 83% of web traffic flowing through APIs, even a single failure can break payments, dashboards, and user experience. Teams that invest in automated API testing do not slow down, they ship faster with confidence. A strong strategy goes beyond checklists. It defines what success looks like, where tests run, how data stays consistent, and how testing fits into CI/CD.

Black box testing techniques are what turn a vague requirement into a specific, repeatable test case. Most critical bugs in production aren’t found by reading code. They’re found by pushing the right input through a feature and observing what comes back. A blank field where the system expected text. Or a value just past the accepted range. Sometimes it’s a combination of conditions nobody thought to test together.

Most founders have a build problem. Very few have solved the distribution problem. That gap is where startups die. When someone comes to me with a startup idea (and this happens more than you’d think), I always ask the same question first.

Unpredictable tests slow pipelines, mask real defects, erode confidence in automation, and, perhaps worst of all, break builds. Here's how to find, fix, and prevent flaky tests.

There is a scenario playing out in QA teams everywhere right now. A team adopts an AI testing tool, runs it for the first time, and gets 300 test cases in minutes. The demo worked. The ROI math looked great. But three sprints later, 60 of those test cases are validating requirements that were updated in the last sprint. Twenty more test a user flow that was deprecated. The AI performed exactly as advertised. The governance system never existed.