Systems | Development | Analytics | API | Testing

Zapier connects to 8,000+ apps. Databox connects to 130+. So why would anyone choose Databox MCP? The answer: they’re built for different things.

Your dashboards are lying to you, not through bad data, but through incomplete data. They show you what you told them to watch. They cannot show you what you did not know to ask. Telemetry-driven performance engineering uses metrics, logs, traces and profiling to detect and diagnose issues that traditional dashboards cannot capture. The failures that hurt most are not the ones you predicted; they are the ones your monitoring was never designed to catch.

Every major layer of the AI stack now has a name. Model providers - OpenAI, Anthropic, Google - handle inference. Agent frameworks - Vercel AI SDK, LangGraph, CrewAI - handle orchestration. Durable execution platforms like Temporal make backend workflows crash-proof.

HTTP streaming – the default transport underneath every major agent framework – was never designed for sessions that survive a tab close or hand off cleanly between participants. Two failures surface consistently in production CX products because of this. Both generate support tickets about conversation state and prompt quality. Both trace to the transport layer. The scenario that illustrates them: a customer contacts support about an order that's partially shipped and partially stuck.

Most teams building AI agents start with HTTP streaming. It's the right starting point. Every major agent framework defaults to it, it gets tokens on screen fast, and for a single-user prompt-response interaction it works well. The question is when it stops being enough - and how to recognise that before it turns into user experience problems, engineering waste, and technical debt that constrains what your product can do.

You can build Data Apps inside Keboola with Kai. But what if you prefer working with Keboola via MCP, in Claude Code, Cursor, or another AI-powered editor? Want to build a JavaScript Data App that Kai doesn't support yet? That's what the Keboola AI Kit is for. It's a set of skills you install into your agent so it knows how to work with Keboola - how to query your data, how to structure a Data App, how to deploy it. Here's how to set it up.

If you’ve used Claude Code, GitHub Copilot, Cursor, or any coding agent, you already know the feeling. You describe what you want in plain language, the agent figures out the steps, and you watch it work. When something goes wrong, it backs up and tries a different approach. Reflect now brings that same agentic workflow to test automation. Through the SmartBear MCP server, any coding agent that supports MCP can connect to Reflect and build tests from high-level objectives.

Before rolling out policy-driven APIs, it's crucial to have a governance framework in place. This framework should clearly outline who makes decisions, how approvals work, and how exceptions are handled. Interestingly, while 71% of organizations claim to have data governance programs, only 25% actually put them into practice. Even fewer - just 28% - have enterprise-wide oversight for AI governance roles and responsibilities.

If we want our apps to succeed, we have to get our buttons spot on. They allow our users to navigate around our apps, show their preferences and define their own personal user journeys. Not only that, they play a crucial role in the overall look and feel of our apps, and enhance our overall brand image if we get them right.

The SwiftUI vs UIKit question may seem like a sticky web of pros, cons and competing nuances. But ultimately, it boils down to one thing: Which framework is best for my specific app? The answer shouldn’t be based on hype or trends. It should be based on your own real-world parameters, like team size, UI complexity and long-term maintenance. Understanding your own development realities is crucial to making the right choice between SwiftUI and UIKit.