Systems | Development | Analytics | API | Testing

We are introducing a new approach for building, deploying, and managing AI applications. This approach features a visual, drag-and-drop model for building AI apps and supports deep integration with enterprise data and application infrastructure.

Organizations are moving beyond simple automation towards a future where systems are intelligent enough to tackle complex tasks with minimal human intervention. Agentic workflows are the driving force behind this shift. According to Gartner, a staggering 33% of enterprise software applications are projected to integrate agentic AI by 2028, enabling them to autonomously make decisions for as much as 15% of routine work.

With data streaming, public sector organizations can better leverage real-time data and modernize applications. Ultimately, that means improving the reliability of services that agencies and citizens depend on, enhancing operational efficiency (therefore cutting costs), and delivering critical insights the moment they’re needed.

The Model Context Protocol (MCP) has taken the internet by storm by rapidly becoming the standard for Large Language Models (LLMs) to communicate with external data sources or tools. MCP provides a structured way to fetch data and trigger workflows through APIs and functions. However, with great power comes great responsibility.

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.

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.

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.