Traditional QA, while foundational to software engineering, is reaching its limits ... What worked yesterday is increasingly not going to work today, and tomorrow's risks cannot be addressed using yesterday's checklists. This is where agentic QA steps in, heralding a transformative approach that integrates autonomous, intelligent agents throughout the entire software lifecycle ...

The use of artificial intelligence brings high hopes and expectations in the technological world, promising changes to the way businesses operate. A staggering 79% of companies are now or will be using AI within the next year according to an August 2025 survey from OpenText and the Ponemon Institute ... However, there are some unexpected challenges ...

Everybody's talking about AI replacing developers ... After years of building AI-powered development tools and watching how professional teams actually use them, I'm convinced the "AI is killing developer jobs" narrative is overstated. The real shift is more nuanced: fewer junior roles over time, but dramatically increased demand for experienced engineers who can work effectively with AI ...

Not long ago, "security" meant building walls — firewalls, intrusion detection, access control lists. While those tools aren't dead, they're not enough. Cloud-native systems don't care about your perimeter, and one misconfigured API or overly generous IAM role is all it takes. The rules have changed, and if your security game hasn't, you're already behind ...

The promises AI is making for DevOps (faster coding, faster debugging, faster reviews) is appealing. But in practice, that speed does not automatically translate into faster delivery or impact. AI often generates more tasks than it resolves: refactors, bug reports, and code suggestions can appear asynchronously from multiple tools, creating floods of new work ...

Most people don't realize just how much of the world still relies on software written decades ago. Banks, hospitals and government agencies rely on systems that were built long before cloud, mobile or AI even existed. That reliance is costing organizations billions each year, not to move forward, but to keep the old code alive ...

When was the last time you actually looked at the API calls in your codebase? Not the ones you wrote yourself, but the ones quietly generated by your AI assistant. Do you know where they point? Are they hitting a test server? Did they skip authentication? Are they leaking something in error responses? You start asking these questions after something goes wrong (and no one knows why) ... The thing is, generative AI (GenAI) is excellent at speeding up how we write code, but it could become a major concern if not thoroughly checked ...