Systems | Development | Analytics | API | Testing

At SmartBear, we’re dedicated to empowering teams by delivering application integrity: continuous, measurable assurance that their software works as intended, even as AI accelerates the SDLC. The AI Test Innovators Award: QMetry + Reflect celebrates customers who are using AI to reimagine how they create, maintain, and manage tests – without sacrificing rigor or reliability.

Test libraries that once felt instant can become harder to manage as they grow: test cases take longer to open, reports take longer to build, and every sprint adds more data to an already bloated Jira environment. That’s usually the moment when a testing leader starts evaluating whether the test management tool installed years ago still fits the way the organization tests today. Your team has already decided to run testing inside Jira.

AI agents review code, triage incidents, summarize tickets, and draft documentation, and the industry has largely decided the help is worth having. Leadership is often pushing teams for AI productivity gains and many teams accept the mandate. The obstacle is what happens next: the agent works on the wrong thing, the time and money spent on it return nothing, and the team ends up less efficient than before it started by creating more work.

AI has changed how fast testing teams can move, yet sorting through duplicate test cases still trips teams up. Ask the SmartBear Zephyr Agent for Rovo to generate test cases for a requirement, and coverage that used to take an afternoon comes back in a single conversation. That speed is a genuine win for teams building faster than ever, but as AI generates more test coverage, how do you make sure all of it stays worth keeping?

Drift detection stays narrow on purpose. It checks contract conformance – structure, status codes, schema – and deliberately leaves workflows and business logic to functional and end-to-end tests. That’s what makes it a fast add to our pipeline to prevent API drift. That narrow scope keeps it cheap to run. Fast execution, low flakiness, low maintenance cost – the kind of checks that make you more confident shipping. Catching drift early is the whole point.

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.