Systems | Development | Analytics | API | Testing

Automated testing is status quo for a majority of software delivery teams today, yet two-thirds of teams say they deploy code without completing all the necessary testing – and that it costs them anywhere between $500,000 and $5M USD annually. That’s according to a recent survey Tricentis commissioned with Censuswide.

The hype for Anthropic’s Model Context Protocol (MCP) has reached a boiling point. Everyone (including Kong) is releasing something around MCP to ensure they aren't seen as falling behind in the ever-changing AI landscape. However, in this mad dash, there remains confusion around MCP and what this standard actually enables. Some see MCP as a total game-changer, and some see it as little more than a thin and unnecessary wrapper. As usual, the truth lies somewhere in between.

Why do one in three data professionals cite integration challenges as the biggest roadblock to AI success? Our latest research uncovers the missing link to AI-ready data.

This article originally appeared on BigDataWire on Feb. 26, 2025. Artificial intelligence (AI) agents are set to transform enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. But the real challenge isn’t building better models. Agents need access to data and tools as well as the ability to share information across systems, with their outputs available for use by multiple services—including other agents.

Nowadays, data-driven testing has become a critical approach for improving test coverage and ensuring software reliability. By executing test cases with multiple sets of data, teams can validate application behavior under various conditions without manually creating numerous test scripts. This enhances efficiency and uncovers defects that might otherwise go unnoticed in static test scenarios. However, data-driven testing also comes with challenges.

Android test automation is the process of using software tools and scripts to execute tests on Android applications automatically.

Kong customers include some of the most forward-thinking, tech-savvy organizations in the world. And while we’re proud to help them innovate through traditional APIs, the reality is that their ambitions don’t stop there. Increasingly, our customers are investing heavily in real-time data and event streaming.

During our recent webinar Quality Horizon 2025, the virtual room buzzed with energy, filled with insightful questions that pushed our thinking forward. But one particular query truly struck a chord, a question that elegantly highlighted a core challenge in AI-driven testing: The observation was spot on. It perfectly captured a critical limitation we’ve seen across the current AI testing landscape.

Front-end testing ensures your application looks and behaves as users expect on every device, browser, and interaction. Whether it's clicking a button, filling a form, or navigating pages, front-end tests validate what users actually experience. But manual front-end testing slows teams down. As the interface evolves, so do the tests, and without automation, keeping up is nearly impossible.

Test automation is essential if you want to move fast without breaking things. But here’s the hard truth: not every test is worth automating. And trying to automate everything is how teams burn time, introduce flakiness, and end up maintaining tests that add zero value. So how do you know what test cases to automate? That’s what this guide is for.