Systems | Development | Analytics | API | Testing

AI code generation is rapidly evolving from a novelty into a key building block of modern software development. According to the Tricentis 2025 Quality Transformation Report, 82% of software professionals are excited about AI agents handling repetitive development tasks, and 84% believe AI will help teams meet increasingly compressed deadlines. Tools like GitHub Copilot and Codex are driving this revolution, offering real-time suggestions and automating boilerplate work.

Most product teams today are very good at one thing: testing what happens when a user types a prompt. Hemraj Bedassee , Delivery Excellence Practitioner,

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In today’s world of software development, API-first has become more than a trend it is a best practice that allows teams to build scalable, modular and best of all future-ready applications. In an API-first approach, the Application Programming Interface (APIs) are not an afterthought, they are envisioned, documented and agreed upon before the development of an project in begins.

The meeting started with a question I wasn't expecting. "Dragan, if AI can write tests now, what exactly will your team be doing next year?" Q1 2025, six months before I founded Quantum Quality Engineering. The executive wasn't hostile, just genuinely curious. Behind that curiosity lurked a more complex question: Do we still need QA leaders in an AI-driven world?

Every test team has asked the same question at some point: “Should we automate this, or just test it manually?” The answer is rarely black and white. That’s why a Manual vs. Automation Decision Matrix exists to bring clarity to the chaos. It helps teams understand the lifecycle of a test case, its complexity, how often it runs, and the value it adds to product stability. With a decision matrix, you can make more informed, ROI-driven decisions.