Systems | Development | Analytics | API | Testing

While every organization races to deploy AI agents faster, a quieter crisis is compounding in the background, and it will play a large part in determining who survives the agentic era. The numbers are stark. Too many executives see AI governance as a brake on innovation or something to figure out later, after the speed problem is solved. With agentic AI, that's backwards.

AI is reshaping test automation fundamentals. Features that once required hours of manual scripting can now adapt automatically to UI changes, generate realistic test data on demand, and help teams predict which tests matter most. For QA engineers evaluating automation platforms, understanding how AI capabilities differ has become essential. This comparison examines SmartBear TestComplete, Tricentis Tosca, and Ranorex through their AI-powered features.

In November 2023, I flew to Germany to speak at Agile Testing Days, one of the leading software testing conferences. My keynote was called “10x Software Testing.” I knew there would be skepticism in the room.

And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.

Conversational Analytics in BigQuery in preview brings a sophisticated AI-powered reasoning engine directly into BigQuery Studio.

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

Recently, I had the pleasure of sitting down with Cristiano Caetano, VP of Product at Katalon, to explore his journey in software testing. It was a path that began with frustration, evolved through innovation, and now looks toward an exciting AI-driven future. Cristiano's insights shed light not only on his personal career path but also on the broader evolution and future of software testing.

"We were wasting too much time debating the accuracy of numbers as opposed to using that time to make decisions." That's how Satty Saha, Group CEO of CreditInfo-a credit bureau operating across 30+ countries-described the moment he realized his organization had a data problem. Not the kind of data problem you'd expect from a company whose business is data and analytics. An internal data problem.

Executive Summary: As enterprises integrate AI and large language models (LLMs) into their data workflows, the need for predictable, secure, and auditable database interactions has never been greater. Deterministic queries—particularly those encapsulated in stored procedures—provide the guardrails necessary for both human analysts and AI systems to access sensitive data safely.