Systems | Development | Analytics | API | Testing

Picture the scene. You're sitting in yet another budget meeting, and someone inevitably pipes up with the golden question: "Why are we paying for seven different tools when we could just use one?" Everyone applauds. Confetti bursts from the ceiling. There’s a cake for some reason. That’s the siren song of vendor consolidation. It's everywhere these days, and honestly? It makes sense. Finance loves it because it’s cost savings that can be easily represented as a budget line item.

The Model Context Protocol (MCP) is quickly becoming a foundational layer for AI systems. It enables large language models and AI agents to interact with external tools and data sources over standardized JSON-RPC interfaces. By doing so, MCP transforms how intelligent applications consume APIs. Reading local files, controlling IoT devices, orchestrating backend workflows—MCP servers act as structured gateways between AI and your business logic.

This is part one of a series exploring how Kong AI Gateway can be used in an AI Agent development with LangGraph. The series comprises three parts.

Your data warehouse holds untapped potential for generative AI (GenAI), but there's a problem: most systems lack the right connection to make this work seamlessly. Enter the Data AI Gateway - a middleware solution designed to bridge the gap between massive datasets and AI systems. This tool not only streamlines integration but also tackles key challenges like data security, real-time access, and cost management.

Turn quality into a strategic advantage—accelerate outcomes with Sauce intelligence.

Turn quality into a strategic advantage—accelerate outcomes with Sauce intelligence.

This article originally appeared on DevPro Journal. We’re sharing it here for our audience who may have missed it. QA’s job has always been simple: find the bugs before your customers do. There was a time when that meant checking every corner of an application by hand, clicking through countless possible user scenarios. Today, with software moving faster and expectations higher, a tiny slip can cost your business. Testing that’s quick, precise, and thorough has never been more critical.

Staging environments are often looked at as the testing ground ahead of the “real” production environment. The idea is simple – build a duplicate of your production environment, run your tests, and ship with confidence. But the reality of using staging in the real world as part of a holistic API testing strategy is rarely that clean. No matter how meticulously you mirror production services, staging always falls a little short.