Quality Assurance (QA)

From fragile browser automation to structured, agent-native web interactions The Browser Was Built for Humans. What About AI Agents? For more than two decades, websites have been designed around a simple assumption: a human user interacts with a visual interface. Buttons are clicked. Forms are completed. Menus are opened. Information is discovered through page layouts,

Playwright MCP vs Vibium vs Selenium + Robot Framework: A Hands-On Guide to Agentic QA AI can interact with a browser. But can it produce trustworthy test evidence? Let’s investigate. The experiment: one task, three approaches Imagine giving an AI agent access to a web application and asking it to: Log in with a valid

When Software Moves Faster Than Our Ability to Prove It Works Software engineering has spent decades learning how to manage technical debt. We know what happens when architecture is neglected. We know what happens when shortcuts accumulate. We know that a system can continue working while becoming increasingly difficult, expensive, and risky to change. But

Self-healing automation promises fewer broken tests. But what happens when the test heals itself precisely when it should be failing? The Green Pipeline That Should Have Been Red Imagine this. It is Monday morning. A developer changes the checkout page. The CI pipeline runs 300 automated tests. Everything is green. 300/300 PASSED. The team is

The challenge is no longer only how fast we can build software. It is how fast we can build enough evidence to trust it. Software engineering has always been constrained by a simple reality: software takes time to build. A developer needs to understand a requirement, design a solution, write the code, debug it, review

Software testing has evolved dramatically over the past decade. Applications are now deployed continuously, Agile delivery cycles are shorter than ever, cloud-native architectures are becoming standard, and organizations are under constant pressure to deliver faster while maintaining high quality. At the same time, automation has become one of the most important pillars of modern software

Applications developed by Artificial Intelligence are fundamentally changing software engineering. In these systems, AI is not an auxiliary feature or a plugin. It is the core decision-making engine that defines application behavior. This creates a structural shift in quality assurance. Traditional QA assumes deterministic logic, stable outputs, and rule-based validation. AI-developed applications violate all these