Systems | Development | Analytics | API | Testing

Your AI investment has a governance gap, and it's called testing

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.

Smarter, faster CI/CD for the new AI-powered development loop

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.

New MCP tools for Declarative Pipelines

Qlik's MCP server just got three new lookup tools built for data engineering. They connect directly to your Qlik Cloud tenant, so coding agents can pull the real project values a pipeline needs instead of working from an empty template, find spaces and data connections by name, and browse the tables and views available on a connection, just by asking in natural language. That means easier declarative pipeline creation, with real tenant context built right into your prompt for faster, more accurate iteration.

Agentic AI Just Rewrote the Data Engineer's Job Description. Here's What IT Leaders Need to Know.

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.

What's Driving the Great AI Re-Architecture?

As enterprise AI scales, traditional data architectures are reaching their limit. Workloads are becoming more distributed, data movement is accelerating, and tech leaders face growing pressure to justify AI spend while delivering real business impact. Chief Technology Officer Sergio Gago breaks down key findings from Cloudera’s latest global survey of enterprise architects, data architects, and cloud leaders.

Cloud Testing's Key Role in Sustainable Software Development

Cloud testing offers a practical path for reducing the environmental impact of software development. By eliminating the need for dedicated physical hardware, teams can cut energy consumption and electronic waste. Virtualized environments scale up only when needed, replacing racks of underused servers with efficient, on-demand resources.

AI Chatbots: Transforming Performance Testing

Until recently, performance testing workflows meant complex scripting, manual maintenance, and slow feedback. Now, the adoption of AI chatbots in QA and DevOps is prompting a fundamental shift. Teams using AI-driven testing tools are seeing significant reductions in test cycle times and improvements in defect detection. These are not incremental improvements, but shifts that are redefining benchmarks for speed and coverage.

Why More Brands Are Choosing TikTok Launchpad Services for Faster Growth

A brand can have a great product, a strong website, and a clear customer base and still struggle to gain traction on TikTok. The platform looks simple from the outside, but successful growth usually depends on getting several things right at once: content, creators, product positioning, audience targeting, TikTok Shop, and ongoing testing.