Systems | Development | Analytics | API | Testing

It’s easy to create APIs very quickly. However, creating APIs that are reliable when used in challenging distributed systems is much harder. Many development teams are moving quickly to ship features to the market, which has led to the verification vs validation distinction becoming the middle ground between a successful launch and an incident that could occur from a production system.

Today’s software ecosystem consists of various devices, integrations, and user environments. The same application can have different behaviours in each environment, regardless if it had passed through unit, integration and system testing stages. Therefore, beta-testing is necessary. Beta testing is the bridge between an internal QA team and a company publicly launching its application.

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.