Systems | Development | Analytics | API | Testing

July was an enterprise month. Most of what shipped answers the questions that come up once a test team grows past a handful of people: how do we hand documentation to an auditor, where is the evidence for this step, who changed this result and when, and how do we run the same suite against a feature branch without rebuilding the integration every time.

Claude Code shipped workflows: reusable, versioned instructions an agent can follow instead of improvising a process from scratch each time. We used that feature to hand Claude Code our entire QA job. Not "write a test case." The whole loop: read the requirement, design the coverage, run it on the platform, file the defect, report back with evidence. The kind of work a QE does in a day, compressed into one skill triggered with a sentence.

At some point a product manager or a designer is going to turn to the most user-focused person on the team and say: can you run a usability test on this? That person is usually a manual tester, because manual testers already spend their days thinking about how real people use software.

May 2026 marks a turning point for how QA teams think about automation. This month's releases push Katalon True Platform closer to a world where you describe what you want to test, and the platform handles the rest: from generating test cases to running them, then handing you a ready-to-use script. Here is everything that shipped in May.

June's releases share one thread: less setup between what you already know and a working test. Production Insights can turn a single real user session into a test case without a full Journey Map first. Katalon AI Assistant now reads the docs and boards your team already works in, shows its reasoning as it goes, and reaches apps your CI never could. Test management got safer to run at scale, with version history, ALM sync, and encrypted secrets. Here's everything that shipped.

You opened the AI assistant for the first time with a fair amount of hope. You typed "write test cases for the login page." You got eight test cases back in about three seconds. Valid login. Invalid password. Empty username. Empty password. The kind of list you could have written in your sleep, missing every scenario that actually matters for your product. So you closed the tool and thought: this is fine for the basics, but it does not really get testing. AI is overhated.

If you only follow the loudest headlines, it is easy to believe AI is about to wipe out manual testing. Katalon's State of Software Quality Report 2025 tells a more useful story, and it comes from inside the industry: over 1,500 QA professionals, from individual contributors to senior executives, across North America, Europe, and Asia-Pacific.

You have tested hundreds of features. You know the drill. Open the test case, write the preconditions, list the steps, fill in the expected result, run it, compare. Pass or fail. Move on. Then someone hands you an AI feature. A chatbot. A "summarize this ticket" button. A search box that answers in full sentences instead of returning a list of links. You open your test case template, you get to the "expected result" field, and you stop.

A conversation with Gastón Marichal, QA Manager at QAlified, on why the hardest part of agentic QA has nothing to do with the technology. In the first installment of this series, we heard from Huy Tieu, a Katalon product manager building an AI quality companion from the inside. This time we wanted the other side of the table: someone who has to make agentic QA work for real client teams, on real delivery timelines, with real consequences if it goes wrong.

Enterprise software today isn't just complex, it's mission-critical. A single production issue can disrupt operations, impact revenue, and erode customer trust overnight. Yet despite years of investment in enterprise test automation and growing QA headcount, many organizations still ship broken software and miss release windows. The uncomfortable truth? Enterprise software doesn't fail because teams aren't testing enough.