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A failed payment is never just a technical failure. It is the moment a customer loses confidence in your platform, abandons their purchase, and often does not return. Unlike most bugs, a payment failure has a direct and immediate cost. This guide gives you the test cases to prevent that. Below you will find over 50 test cases organised by type and filterable by category, guidance on using AI to speed up your test suite, and a pre-launch checklist you can work through before every release.

Play Quality People - Huy Tieu: The Evolution of Agentic QA: From scripts to Harnesses 17: 18 A conversation with Huy Tieu, Senior Product Manager at Katalon, on why the scripted testing model broke, what replaces it, and the one experiment every QA leader should run this week. The term "Agentic QA" is everywhere in 2026. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by year's end, up from under 5% in 2025.

AI-assisted development means more code, faster release cycles, and a testing surface area that keeps expanding, but QA team size hasn't kept pace. The result is a pressure most QA engineers know well: you're not behind because you're slow. You're behind because the math no longer works. Most tools respond by adding AI at the margins: a suggestion feature here, a copilot shortcut there.

Software testing is going through its biggest shift since teams moved from manual to automated testing. The difference this time? The AI isn't just helping testers write scripts faster. It's making decisions about what to test, when to test it, and what to do when something breaks. This is Agentic QA. And if you're a QA leader, engineer, or anyone responsible for software quality, it's a concept you need to understand now, not in six months.

Software testing has a fragmentation problem. Most teams run test generation in one tool, execution in another, defect tracking in a third, and pull together release decisions from whatever they can stitch together at the last minute. Every handoff between tools is a gap where context gets lost, work gets duplicated, and quality suffers. Katalon True Platform closes those gaps.

Before your team invests in any AI testing capability, there is one question worth asking plainly: does this platform get smarter the more you use it, or does it start from scratch every single time? The term "intelligent test automation" is used generously across the industry right now. Nearly every testing tool has added AI features: auto-generated test cases, smart locator healing, suggested assertions, anomaly detection. But intelligence, in any meaningful sense, requires memory.

Picture this: it's the Thursday before a major release. The VP of Engineering asks a simple question in the planning meeting: "Are we confident we can ship Friday?" The QA lead opens four dashboards, pulls an export from the test management tool, cross-references it with execution results from a separate environment, reconciles defect counts in the bug tracker, and 40 minutes later delivers a hand-built status summary that is already slightly out of date. The team isn't slow. The team isn't incompetent.

ATLANTA, GA — April 07, 2026 – Katalon, the category leader in AI-augmented software testing, today announced the launch of Katalon True Platform — a unified software quality platform that combines purpose-built AI agents with the governance, traceability, and human oversight that AI-driven development demands. As AI accelerates how software is written, testing has become the critical bottleneck.

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.

You have an AI coding assistant open. You describe a function in plain language, it generates 40 lines of clean, well-structured code in under ten seconds, you review it briefly, it looks right, and you ship it. That workflow is now routine for millions of developers. The speed is real. The output looks authoritative. The problem is that looking right and being right are not the same thing.