Systems | Development | Analytics | API | Testing

In today's digital landscape, APIs are the backbone of modern applications, and AI is the engine of innovation. As organizations increasingly rely on microservices and AI-powered features, the API gateway has become the critical control point for managing traffic. But as LLM/GenAI and MCP requests flow through these gateways, they bring a new wave of security challenges.

Here’s what you need to know: Bottom Line: Treat LLMs as untrusted clients. Secure database access with strict governance, API controls, and robust monitoring to mitigate risks without sacrificing productivity.

Shipping high-quality software quickly is challenging. QA professionals are facing pressure to test more, faster in a world where GenAI is pushing delivery – all while trying to cut costs. For years, manual testing and traditional automation tools like Selenium have been the standard. But both come with challenges. Manual testing alone can be slow and prone to errors, while Selenium and similar tools require coding expertise, need constant script maintenance, and are easily broken by UI changes.

Advances in large language models (LLMs) and their widespread accessibility have transformed both what software can do and how we build it. The use of LLMs has quickly evolved from simple single-turn interactions to AI agents that reason, use tools, manage state, and operate autonomously.

Delivering value now with data integration, the right data platform, and generative and agentic AI.

We keep hearing the phrase AI testing used in very different ways, often in the same meeting.

You’re in the cockpit of a small plane, cruising a few thousand feet in the air. Then, out of nowhere, the airspeed dips and an alarm rings out. The nose drops, and you're in a full-out stall by the time instinct kicks in. You pull back on the yoke, trying to steady the plane, stop the descent and patch things up midair. But that’s exactly the move that seals your fate, sending you into a deeper spiral.

The emergence of AI has created uncertainties in the software and technology world. As it encroaches into the conventional application test-automation space, QA professionals might feel threatened or even cornered. While it is true that AI is changing traditional testing roles, it also opened new opportunities in the data testing space. But what does AI rely on? Obviously, data!

Stop looking for an AI Analytics tool. Start looking for an analytics protocol. That advice sounds counterintuitive. Everyone’s searching for “the best AI analytics platform” or “which BI tool has the best AI.” But that framing misses what’s actually happening in the market, and why most AI analytics implementations fail to deliver on their promise.

If you’re evaluating MCP servers for your analytics stack, you’ve probably noticed that “MCP support” can mean very different things depending on the vendor. I’ve been working with both platforms, and the distinction matters more than most comparison articles let on. Supermetrics and Databox both offer MCP implementations, but they’re built for different jobs.