Software Testing Unleashed - QA, DevEx & Quality Engineering

What separates a handful of agents from a real agentic system? With Nishan Portoyan I talk about exactly that line, and why most teams are still on the wrong side of it. We walk through a full testing pipeline where eight to twelve agents handle everything from requirement review to performance test conversion, and I keep coming back to the question of where the human has to step in and why the AI simply cannot replace that judgment. Nishan is direct about what these systems cannot do: they do not think, they gather information and hand it back, and anyone expecting otherwise will run into trouble fast.

What happens to quality when anyone can generate code with a prompt but nobody checks whether it actually works? With Olivier Denoo I talk about why that question puts expert testers in a stronger position than many managers currently assume. We get into the real risk of AI that always sounds right, the edge cases a lost suitcase in Brussels reveals, and why the hardest unsolved problem in testing is still the same one Olivier ran into three decades ago: describing precisely what you want. The skills I keep coming back to in this conversation are business understanding, communication, and the ability to spot what the AI confidently got wrong.

Why do we rush to fix problems before we actually understand them? With Nirmala Saneechur I talk about Lean management, what it means to deliver quality at the right time rather than just on time, and why spending a full day understanding a problem is not wasted time. We get into what waste really means in a software project, including the story of 15 features a client never used, and how working in silos creates the kind of back-and-forth that slows everything down.

Slow systems don't just annoy users, they cost companies real money, and most teams only find out when the phone starts ringing. With Rao Dhaligadoo I talk about what it actually takes to catch performance problems before customers do. We get into how a 70 percent CPU threshold can trigger an automated chain of monitoring, ticket creation, and root cause analysis, and why that matters more than it sounds. Rao also shares what it feels like to spend 45 hours in a war room at a bank, watching a CIO walk in at 1 a.m. while the whole team sits in silence, and how that experience shaped the way he thinks about proactive testing.

Forty percent of automation time lost to flakiness and maintenance: that is the reality Lilia Gargouri describes. I talked with Lilia about end-to-end test automation at scale, and what struck me most was how far back the real solutions reach: a single HTML attribute added in 2017 still pays dividends today, simply because the team decided to use it consistently and never change it. We get into the full picture, from how to accurately identify every UI element without XPath gymnastics, to why fifteen well-chosen test cases can outperform five hundred noisy ones, to naming conventions and layered library architecture that keep maintenance from spiraling. And for anyone sitting on an old, flaky test suite right now, Lilia has a concrete answer for where to start.