From Gatekeeper to Architect
AI-generated code is shipping faster than QE teams can test it, and the gap widens with every sprint. This whitepaper introduces the Quality Experience Layer, a model where QE engineers encode their expertise into the AI agents developers are already using. See why the fix is not more test scripts, it is a different definition of QE.
- Learn why AI-generated code makes traditional QE pipelines break, not bend
- See how QE engineers author agents instead of writing more test scripts
- Get the framework for encoding your team's judgment into the developer's workflow
Trusted by Enterprise Customers
When AI-Generated Code Outpaces QE Capacity
AI coding assistants have made developers dramatically faster, generating up to 46 percent of code in AIenabled files and helping teams complete tasks up to twice as fast. But this velocity did not bring reliability. AI-generated code carries roughly 1.7 times more defects than human-written code, and nearly half of AI-generated samples contain at least one known security flaw.
When AI doubles the code output, the QE work doesn't double, it more than doubles, because every additional line demands more scrutiny, not less. QE headcounts aren't growing to meet this demand, and pipelines built for human-speed code creation are unraveling.
From Test Scripts to Encoded Expertise
The Quality Experience Layer reframes the job of a QE engineer. Instead of writing, maintaining, and executing test scripts, they author the skills, thresholds, and acceptance criteria that AI agents apply at the moment code is written. Developers get quality intelligence built into their workflow, not a gate waiting
at the end of it. One refined skill reaches every developer's agent at once, and the QE engineer becomes
the human in the loop, reviewing outcomes and continuously sharpening the standard.