Systems | Development | Analytics | API | Testing

By 2025, one in four enterprises using Gen AI will have AI agents in place, and that number will double by 2027. As organizations race to integrate these intelligent technologies, the spotlight is on agentic automation, a transformative approach reshaping how businesses operate. Right now, enterprises are at a key turning point.

Software quality today is not the responsibility of just one team-it’s a shared mission across development, QA, product, and operations. As systems grow more complex and software delivery cycles shrink, test automation has moved from being a luxury to an operational necessity. But adopting automation tools isn’t enough.

Java throws an UnsupportedOperationException when you attempt to modify an object that was designed to be viewed but not changed. Think of UnsupportedOperationException as trying to open a door with the wrong key. The door exists and looks like any other door, but your particular key (or operation) simply isn't compatible with its lock.

Let’s get straight to the point—this post is vital and couldn’t have come at a better time. As QA professionals, we’ve always been the gatekeepers of software quality. But with the rise of AI and LLMs, our role is evolving. Writing evaluations—assessments of AI systems—is quickly becoming a core skill for anyone working with AI products, and soon, this will include nearly everyone in tech.

Data leaders face a high-stakes challenge as stricter global and state-level privacy laws, like the American Privacy Rights Act (APRA), ramp up financial penalties — potentially up to 4% of a company’s global revenue — for compliance failures. Organizations must prepare for increased accountability and standardization in data privacy, sovereignty, and ethical AI governance, all of which demand stronger data safeguards and transparent practices.

In this episode, Nuwan Dias, Vice President and Deputy CTO at WSO2, joins to explore what effective API lifecycle management really looks like. Drawing on his deep experience building API platforms, Nuwan outlines the key pillars of successful API programs—from strategy and governance to security, testing, and deployment. He shares how organizations can operationalize API-first thinking at scale, and the role of dedicated API teams in enabling this shift.

Say you’re a new developer, and you were just hired to test an e-Commerce website before it goes live. You want to make sure that the login function is working. Is it time for a smoke test or a sanity test? Well, it depends. If a login bug was recently fixed, then you’ll want to run a sanity test to check whether users can successfully log in with valid credentials or if the bug broke the login’s functionality.

Load and performance testing are critical aspects of ensuring that web applications and software systems deliver great user experiences, no matter the traffic spike or the complexity of operations in the background.

AI has become the talk of the town nowadays, right? There are tons of AI tools available for different tasks, and new advancements are coming up daily like vibe coding. But how do you actually do vibe coding? Or how do you try out these models? You could use tools like ChatGPT or Claude, but they come with restrictions, and you often need to pay to access full features. What if you don’t want your data to become part of their training models? That’s where open source coding models come in.

With Deloitte predicting that 25% of companies using Generative AI will launch agentic AI pilots or proofs of concept in 2025, is your testing strategy ready for the agentic revolution? This highlights the pace at which the modern software development industry, already demanding continuous operational speed improvements, heightened efficiency, and superior product quality, is turning to advanced AI.