Systems | Development | Analytics | API | Testing

Testing looks nothing like it did five years ago. What once demanded armies of engineers writing brittle scripts now runs on intelligent systems that create, adapt, and analyze tests on their own. AI has rewritten the rules, and the teams that recognize this shift early are pulling ahead of those still patching broken automation night after night. For QA leaders and DevOps directors under pressure to ship faster without sacrificing quality, understanding this evolution is more than academic.

Article Summary: Most teams have adopted AI coding tools, but testing is still manual, so the speed gain rarely survives to release. This post covers why that gap forms, how your team can maintain application integrity, and how QMetry’s AI features, from fast test creation to a Release Readiness Advisor, connect coverage, risk, and release decisions in one system instead of a second disconnected tool.

We said you'd see the evidence quickly.. Here it is. **Kong AI Gateway 2.0 is now generally available**, and it isn't arriving quietly.

Choosing API monitoring tools can be overwhelming, with feature lists and buzzwords competing for attention. When you’re responsible for business-critical APIs, the best tool is the one that delivers real-time, actionable data and fits your system’s actual needs.

Discover how BigQuery Graph enables native graph analytics and AI agent grounding, delivering connected context across enterprise data without moving data.

Sauce Labs is the first test automation platform certified to ISO/IEC 42001. Here's what that actually means for your procurement questions.

Most AI coding tools run in a Linux container somewhere. Codespaces is Linux. Copilot’s coding agent works in an ephemeral, Actions-powered Linux environment. Nearly every agent framework assumes a container that spins up in seconds. None of them can build your iOS app, or have build cache to speed up the builds, or have simulators that can run tests that you can view.

Gartner predicts that 70% of today's data engineering tasks will be fully automated by 2030. I put that number to Tim Garrod, Qlik's Head of Product Management for data integration and quality, on a recent Qlik Insider session, and his answer is the one every CIO, CDO, and VP of IT should sit with: automation doesn't make the data engineer obsolete, it makes the good ones ten times more valuable. AI amplifies skilled judgment. It doesn't replace it.

The question product and engineering teams ask before shipping an AI feature is usually simple: does it work? Is the model accurate enough? Are the outputs reasonable?

Imagine this scenario: your engineering team just pushed a spec update. A field was removed, an endpoint renamed, a new required parameter appeared on the payments API. Nobody told QA. Three days later, your regression suite lights up red across a dozen tests that have nothing to do with the actual bug. They’re failing simply because the tests are stale. Someone spends the next afternoon manually diffing the old spec against the new one, hunting for what changed, then rewriting test steps by hand.