Systems | Development | Analytics | API | Testing

Most business leaders view application rationalization like cleaning out a garage: a painful, one-time chore to throw away junk to save space. It is often viewed strictly as a subtraction game that overlooks business context. Get rid of legacy technical debt to lower application costs and IT spend. But this mindset ignores value. A better way to view your application stack is through the lens of a stock portfolio.

See how AI Columns in Fivetran Activations transform scattered customer signals into clear scores, summaries, and talking points.

Another year is wrapping up, and we’re taking a moment to reflect on what made 2025 a defining year for Kong. With major advances in building the AI connectivity layer and soaring enterprise adoption of agentic systems, this year sparked a hockey-stick surge in demand for the infrastructure that powers intelligent agents. Below is a rundown on the updates, the innovations, and the moments that moved the industry in one year-end recap.

People feel more confident when their decisions are backed by numbers. That’s just human nature. Add a chart or a metric to a conversation, and suddenly an opinion feels more credible. This is one reason companies invest heavily in analytics tools. They’re not just buying dashboards, they’re buying confidence. Confidence that decisions are grounded in reality, not gut feel. But here’s the problem: having data doesn’t automatically make decisions easier.

As organizations expand their AI initiatives, they increasingly need to provide users, be they data scientists, AI/ML engineers, researchers, or application developers, with secure access to interactive development environments such as JupyterLab, VS Code, or other internal tools.

Every digital experience we rely on – from booking cabs to transferring money — runs on dynamic, interconnected software systems. The speed at which applications are evolving is much faster than the traditional test approach can keep up with. Manual scripting breaks whenever there is a change to the user interface; automation will require regular maintenance to fix the automated scripts; and teams are continually losing confidence in the release stability.

Traditional crowdsourced testing has always promised scale, but it’s often come with headaches.

2025 underscored a simple reality: APIs are now expected to serve both human developers and intelligent systems, and the tools supporting those APIs must evolve just as quickly. Major cloud providers (OpenAI, Google Cloud, Azure, AWS, Hugging Face, Cohere, etc.) now earn significant revenue by exposing their capabilities via APIs, which are then chained by other AI systems to build chatbots, copilots, and autonomous agents.

‍As clinical data grows and patient expectations shift toward personalised communication, EHR-CRM Integration has emerged as a foundational capability for modern healthcare systems. On one side, the Electronic Health Record (EHR) system stands as the repository of vital clinical information, documenting everything from diagnoses and lab results to treatment plans.

‍ The healthcare industry stands at the cusp of a revolutionary change, driven by an emerging technology that can do more than just analyse data; it can create it. That technology is Generative AI, or GenAI, and its arrival in medicine is being hailed as the next frontier in personalised, efficient, and predictive patient care.