Systems | Development | Analytics | API | Testing

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.

When you build Elixir applications, you'll likely encounter scenarios where you need to handle large datasets efficiently. Whether you're importing user data from a CSV file, updating thousands of product prices, or synchronizing data from external APIs, performing operations one record at a time can quickly become a performance bottleneck. In this two-part tutorial, we'll start by exploring Ecto's powerful batch update and advanced insert capabilities for handling bulk data operations.

Deterministic masking is essential for any businesses that need to secure application data across multiple non-production environments — since it ensures data is masked consistently everywhere it appears. As CTO of Perforce Delphix, I’ve worked with many companies who need to protect sensitive data while providing realistic data for testing and development. This is especially important in industries like insurance, healthcare, and financial services.

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.

While more and more organizations are seeking to standardize and consolidate their API and AI infrastructure efforts in a unified platform, the reality is that many — especially large — organizations already have disparate API deployments across disparate API gateways. One common API gateway that most enterprises have running at least somewhere is AWS API Gateway, as it is often seen as an easy-to-get-started-with solution for teams that are already running cloud workloads on AWS.