Systems | Development | Analytics | API | Testing

Four ways engineering leaders can grow quality engineering capacity so verification keeps pace with AI-accelerated development.

Ask your coding agent about production crashes without leaving the editor.

AI now writes a growing share of your code, and examiners expect the same proof of testing no matter who wrote it. Here is what the verification gap costs a regulated institution once it surfaces in an audit.

AI agents can write and run tests faster than most frameworks can keep up. Direct control of shared real devices allows these agents to recover when unexpected issues arise during testing.

Why the real device versus virtual device debate is the wrong frame for engineering leaders and what the right one looks like.

Generating code has never been cheaper. Validating it is where teams are losing the most time, and every handoff between tools makes it worse.

More code hasn't meant faster releases. With AI accelerating code generation, verification has become the bottleneck.

Browse real devices, stream logs, run Appium tests, and generate test code with AI, all from inside the editor your team already works in.

Every iOS team is about to hit a testing cycle they can't skip. And waiting until production is the expensive way to find out.

From breakpoints on your own machine to crashes only real users ever trigger: nine tools, one verdict for each layer of the stack.