Systems | Development | Analytics | API | Testing

With the EU AI Act now in force, compliance is no longer about aspirational ethics or last-minute checklists, it’s about operationalizing quality assurance at every stage of your AI lifecycle.

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.