Systems | Development | Analytics | API | Testing

After spending hours or even days manually testing your software, you may be wondering whether or not there’s a better way. The good news? There is.

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.

Move beyond analytics and activate trusted warehouse data across your business.

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.

With Valentine’s Day fast approaching, love is in the air. And apparently, so are breakup emails. This isn’t just about one pricing change. It’s about a pattern. Some tools promise “free forever” to get you invested, watch you build workflows, and then change the rules. They know you’ve onboarded your teams, documented your APIs, and integrated the tool into your daily work. By the time they spring the paid tier on you, switching feels painful.

Nothing kills confidence faster than a release that breaks the moment real users touch it. That’s exactly why quality assurance has evolved from a last-minute task into a core engineering discipline. When teams search for top QA tools, they’re no longer looking for basic bug tracking or manual checklists. They want powerful software testing tools that automate validation, integrate with CI/CD pipelines, and scale with modern development.

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.

Every business wants to mature rapidly. For software testing and QA professionals, terms such as low-code application testing, codeless tools, and automation will definitely ring a bell. From a market perspective, a product perspective, and especially a tech stack perspective, ensuring quality is critical in software development.