Systems | Development | Analytics | API | Testing

If you take a step back and think about today’s software development landscape, you could argue that documentation is just as important as the code itself. That’s because traditional documentation workflows — where documentation is manually updated, stored separately from code, and thus often falls out of sync — have started slowing teams down and leading to outdated or inconsistent information.

When was the last time your AI assistant confused a memo from the CEO with 'Game of Thrones' plot lines? Have you ever asked a chatbot a question, only to receive an answer that was not only wrong but hilariously outdated? Imagine asking about the latest iPhone model and getting a response detailing the iPhone 3G. We've all had moments where technology gives us a chuckle—until it's crunch time.

Observability has become critical to ensuring the effective monitoring of application and system performance and health. It focuses on understanding a system’s internal state by analyzing the data it produces in the context of real-time events and actions across the infrastructure. Unlike traditional monitoring, which mainly notifies you when issues arise, observability offers the tools and insights needed to determine not only that a problem exists but also its root cause.

Kong is ranked number 1 on the G2 Momentum Grids for API Management and API Development for Spring 2025. And users seem to agree with the glowing assessment: Kong is currently the highest-rated tool based on user satisfaction in the categories of API Management and API Development on G2. We think there are plenty of reasons why Kong is king when it comes to API management and API development. But don't just take our word for it.

Recent leaks have once again brought API and data security concerns into the spotlight. Specifically, the leaks pointed at the following as major areas to consider when thinking through your API and data security strategies: When considering the above, it’s critical to think about best practices across every stage of the API lifecycle, starting with the initial phases of API design and testing.

Software development has always evolved with new paradigms to meet the growing demands of modern systems. One of the most significant shifts has been the adoption of microservices. Emerging in the early 2010s, this architectural pattern moved away from monolithic applications in favor of smaller, independent services that interact with each other over a network.

Today, we’re excited to unveil Kong AI Gateway 3.10! This release introduces new functionality to enhance AI governance, reduce LLM hallucinations, and accelerate developer productivity when working with AI. Read on to learn more.

The excitement around large language models (LLMs) and agentic AI is justified. These systems can summarize, generate, reason, and even take actions across APIs — all with minimal human input. However, as enterprises race to integrate LLMs into real-world workflows — especially when those enterprises operate in regulated environments and/or deal in sensitive data — one fundamental question looms large.

AI is quickly transforming the way businesses operate, turning what was once futuristic into everyday reality. However, we're still in the early innings of AI, and there are still several key limitations with AI that organizations should remain aware of to ensure that AI is being leveraged in a safe and productive way.

MCP is a new way to integrate LLMs and AI agents with third-party data sources and APIs. It significantly improves how we build tool integrations by eliminating duplicated code and providing a centralized interface for multiple agents to access shared tools. Today, we’re excited to announce the release of Kong’s MCP Server for the Kong Konnect platform. This empowers customers to integrate AI agents and query LLMs to discover APIs, services, and traffic analytics in real time.