Systems | Development | Analytics | API | Testing

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.

"Agentic" is the word of the year in software marketing. Every product that sends an automated email is agentic now. Every tool with a chatbot interface is powered by AI agents. The term has been stretched to the point where it tells you almost nothing about what a product actually does.

If you have ever been asked "how do we know we tested everything?" and did not have a clean answer, a requirements traceability matrix is what you were missing. It is one of those deliverables that sounds bureaucratic until the moment you actually need it: an audit, a release sign-off, a requirement that slipped through without a single test covering it. Then it becomes the most useful document you own.

AI can write a test case in seconds. It looks clean. It is structured correctly. It has a name, preconditions, steps, and expected results - everything a good test case should have. And that is exactly the problem. A polished test case is not the same as a correct one.

API testing has become one of the highest-value activities a QA team can invest in. Because APIs operate at the business logic layer, below the user interface and above the database, tests written there are faster to execute, more stable across releases, and far cheaper to maintain than their UI counterparts. In the test pyramid, API tests occupy the middle tier: broader than unit tests, but a fraction of the cost of end-to-end UI suites.

If your team runs automated tests in Katalon Studio and relies on Jira test management to track defects, you already know the friction: a test fails, a tester screenshots the error, opens Jira, creates a ticket manually, pastes in the log, and attaches the file. Multiply that by dozens of failures per sprint, and you have a process that eats hours and invites human error. Katalon Jira integration eliminates that bottleneck entirely.

Writing test cases sounds simple until you're actually doing it. You read a requirement, try to figure out what "done" even means for it, write out steps, realize you missed three scenarios, go back, revise, and by the time you feel okay about it, the sprint has moved on without you. This happens to every team working at any real scale. It is just what manual test case creation costs you, and it is the problem autonomous test generation was supposed to solve at a category level.

There's a quiet frustration that lives inside most QA teams, and almost nobody talks about it out loud. You know your product better than anyone. You can walk through a customer journey in your sleep. You spot a broken flow in seconds just by using the app the way a real user would. But the moment someone says "can you just automate that test?" the conversation shifts to a language you never had to learn. Selenium. Locators. Frameworks. Script maintenance. XPath. Java.

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.