Systems | Development | Analytics | API | Testing

API endpoints process requests that power our digital world—from social feeds updating in real-time to enterprise systems syncing global inventories across continents. APIs comprise over 57% of Internet traffic, yet API endpoints often remain misunderstood. This leads to design inconsistencies, brittle integrations, and costly maintenance issues. Sound familiar? If you've merged microservices and untangled mismatched status codes, you know the pain.

While AI is revolutionizing the future of nearly every industry, it’s also created a unique set of challenges and liabilities that will need to be addressed as the area grows. Enter AI governance: a set of rules and best practices to ensure that AI is used effectively, securely, and responsibly. But what exactly does that mean, and why is it so crucial for businesses?

If you’ve been using Rest Assured for API testing, you know how powerful it is. The syntax looks simple and easier to understand, but things get interesting when you have to write test cases and mocks for a microservices application that has more than 2 services. In this blog, I am exactly sharing my pain of writing Rest Assured test cases for a microservices application and why I switched to Keploy not because I am working here, but to show you the real pain points Keploy solves.

Modern technology (APIs, Microservices, Cloud Platforms, Mobile Apps) is rapidly changing and becoming a target for attackers as they continue to advance and become more sophisticated. Organizations use penetration testing (a form of ethical hacking) to identify weaknesses in their systems and address them before they can be taken advantage of.

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.

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.