Systems | Development | Analytics | API | Testing

Voice is the most commonly used form of communication for humans, and it is also the most information-dense. Plus, for the first time in history, generative AI has made it programmable to the extent that an AI voice agent can hold conversations almost indistinguishable from humans. But what exactly are AI voice agents, how do they work, and how can enterprises build AI voice agents for their workflows? Learn all this and more in this blog!

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.

Organizations everywhere are in hot pursuit of competitive advantages, seeking out and implementing artificial intelligence technologies ranging from GenAI to sophisticated machine learning systems. Yet, despite massive global investments that are projected to reach $375 billion in 2025, many enterprises remain disappointed with their AI initiatives’ real-world results. Why is it that so many AI projects are failing to deliver on their promise? The answer isn’t in the algorithms themselves.

Teams are spending as much as 71% of their time on administrative tasks and manually entering data. But what if there was a way to automate all their repetitive work so they could focus on performing higher-order tasks, creating value, and driving actual ROI? That’s what AI agents can do for you.

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.

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.

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.

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.

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.