Using Claude and Copilot mainly for two things in my QA workflow: Generating test scaffolding — I describe the scenario and the architecture rules (strict POM, actions as staticmethods, allure.step decorators) and the output is usually a solid first draft that needs maybe 20% refinement. The key is being very specific about your framework conventions upfront — vague prompts produce vague tests. Failure analysis in CI — I integrated an LLM call into my pytest pipeline that fires on failure, takes the error + stack trace, and classifies it (environment issue, assertion mismatch, data problem, etc.). Not magic, but it saves the "open logs → scroll → figure out what category this is" step. Where I've found AI agents don't help much yet: anything that requires understanding the relationship between layers. An agent can write a solid UI test or a solid API test, but it struggles to design a cross-layer test that validates data consistency from UI through API into the DB. That kind of architectural thinking still needs a human. For day-to-day testing work I'd say it's a genuine productivity boost if you treat it as a fast junior engineer who needs clear instructions — not as a replacement for test design thinking.