Systems | Development | Analytics | API | Testing

For too long, the Model Context Protocol (MCP) has operated on a principle of open access: connect an AI agent to an MCP server, and it gets access to every single tool that server offers. While this approach is simple for initial experimentation, it quickly becomes a liability in production.

Most teams today are expected to move faster and be data-driven, but getting clear answers about performance is still harder than it should be. Even simple questions often require jumping between dashboards, piecing together insights manually, or relying on a small group of data experts to dig in. The process can be slow, and it often leads to more questions than answers.

A practical guide to building traceability, governance, and evidence across the AI lifecycle.

Your BI team didn't sign up to spend 69% of their time on repetitive data preparation tasks. Yet this is the reality for most data teams drowning in support ticket backlogs while strategic initiatives languish. Every hour spent manually updating schemas, troubleshooting failed data loads, or running ad-hoc queries is an hour not spent on the analytics that actually drive business decisions.

In the rapidly evolving landscape of AI-driven development, the Model Context Protocol (MCP) has emerged as the critical standard for connecting AI applications to the data and tools they need. We are excited to announce the Technical Preview (TP) of Kong MCP Registry, a major milestone in our mission to provide the most comprehensive platform for modern API and AI management.

A mid-sized enterprise had a straightforward but powerful idea: use their locally-hosted AI model to automatically generate summaries of employee performance review data stored in their SQL Server database. The workflow seemed simple enough: The reality? This "simple" integration touches on some of the thorniest problems in enterprise software: database security, API orchestration, authentication, timeout management, and reliable data transformation.

AI fosters speed. Transparency fosters confidence. AI for QA testing is suddenly everywhere. Every tool claims it’s “AI-powered.” Every demo promises smarter test generation, faster maintenance, and fewer bugs. Plus, with AI accelerating the pace at which developers write and ship code, QA leaders are under growing pressure to keep up. It makes sense that teams are looking for AI for QA testing. But here’s the uncomfortable truth: AI in QA only works if you can trust it.

Your data team is drowning. They spend 80% of their time on repetitive reporting and only 20% on strategic analysis. You hired them to be analysts, but they’re stuck being report builders. Every Monday morning is the same: pull the numbers, update the spreadsheet, format the email, send it out. Rinse and repeat.