Systems | Development | Analytics | API | Testing

In today’s data-driven world, companies depend on accurate and trustworthy data to make better decisions. ETL (Extract, Transform, Load) testing is a key factor in data reliability, as it ensures that data is accurately extracted from multiple locations, transformed correctly, and loaded into the target system correctly without loss or error.

Imagine pushing a new release, only to have a crucial API endpoint break in production — leading to a service outage and angry customers. According to one recent survey, API failures account for more than 35% of production incidents in microservices environments. Reliable API testing is not optional anymore.

You know that frustrating moment when something goes wrong at work, and everyone immediately rushes to quick fixes without truly understanding the root of the problem? This is where root cause analysis (RCA) comes in to help. Let me guide you through everything you need to know about this effective problem-solving method that can transform how you address challenges in your organization.

This is the sixth post in a series about reasons to attend API Summit 2025. Check out the previous post here. APIs empower every enterprise function, starting from seamless customer experiences to efficient internal operations. As a matter of fact, APIs are the foundational technology that fuels the advancements in AI, MCP, agentic, and autonomous systems. They're the critical connective tissue that allows data, intelligence, and actions to flow securely and reliably across ecosystems.

APIs are most commonly and widely used in modern applications. To build the complete application frontend, backend, and databases are required, and to make proper communication, the APIs are required. But what happens when the backend isn’t ready or third-party APIs are unstable?. This is the point at which the use of API mocking comes in. By using the API mocking, developers can build, test, and debug their applications or products faster without waiting for real APIs to be live.

OpenAI's new Agent Builder is revolutionizing how businesses create AI-powered workflows with its intuitive drag-and-drop interface. But here's the challenge that every enterprise faces: how do you give your agents access to internal databases without exposing credentials or compromising security?

In this post, Kong Champion Sachin Ghumbre shares his journey of transforming a complex GenAI application from a state of operational challenges to streamlined control. Discover how Kong AI Gateway provided the enterprise-grade governance needed to secure, optimize, and scale his GenAI solution, tackling issues from escalating LLM costs to prompt injection risks.

You’ve probably been there: your AI coding assistant just generated what looks like a perfect solution to your problem. Decent code quality, reasonable structure, and even some comments. You run it, and… it works. So you ship it. Three weeks later, your production logs are full of 500 errors from edge cases the AI never considered, or worse, you discover the code has been making unvalidated database calls that could have been prevented with basic input sanitization.