Systems | Development | Analytics | API | Testing

In my previous post, we discussed how we can implement a basic AI Agent with Kong AI Gateway. In part two of this series, we're going to review LangGraph fundamentals, rewrite the AI Agent and explore how Kong AI Gateway can be used to protect an LLM infrastructure as well as external functions.

Securing APIs for legacy systems is challenging but critical. Older systems often lack modern security features, making them vulnerable to attacks. Here's how you can protect them effectively.

If you've ever tried building an API with plain JavaScript and found yourself drowning in bugs, weird errors, or spaghetti code, yo, you're not alone. That’s why so many devs are leveling up their backend game by mixing Node.js with TypeScript. It's like going from playing Minecraft in creative mode to building actual skyscrapers: more control, better structure, and way less chaos.

Enterprises like yours manage terabytes to petabytes of data daily. Collecting and storing this massive volume of information is already complex. But the real challenge lies deeper. It's in delivering data effectively, securely, and in ways that empower your teams to innovate. This blog will deep dive into the current state of enterprise data automation and examine the limitations of legacy solutions. Then, we will explore how top-performing organizations approach automation in their industries.

If you have spent some time developing Flask applications, you have probably encountered terms like request, session, current_app, and g. You might even use them daily. But have you ever stopped to think about how Flask makes these seemingly global objects available exactly when you need them, especially in a multi-threaded web server environment? Well, the magic lies in Flask's context system. In this article, you will learn what contexts are in Flask and how to use them with practical examples.

Automating API documentation can save time, reduce errors, and improve efficiency in enterprise systems. Here's why it matters and how it works: Why It’s Important: APIs are crucial for enterprise operations, but manual documentation often lags behind code updates, leading to errors, delays, and increased support costs. Challenges of Manual Documentation: It’s time-consuming, prone to human error, and pulls developers away from critical tasks.

TL;DR – No flaky builds because of “data‑center air” at Bitrise! We built our own chassis that can talk to the Bitrise scheduler, can pre‑cool itself, and squeezes 32 Mac minis into a 6 rack unit oversized pizza box—while looking downright gorgeous doing it. We call it the mini Orchard.

Rate limiting and throttling are two key strategies for managing API traffic in multi-tenant systems. While both aim to maintain performance and prevent misuse, they work in different ways: Rate Limiting: Sets strict caps on the number of requests a user can make within a specific time frame. Extra requests are rejected outright with an HTTP 429 status code. Throttling: Dynamically adjusts request processing speeds based on system load. Extra requests are slowed down or queued rather than blocked.

We're excited to announce the general availability of Konnect Debugger, formerly known as Active Tracing during its tech preview phase. This powerful debugging and observability solution in Kong Konnect has evolved from a focused tracing tool into a comprehensive debugging platform.