Why can I tell a test is flaky but never find out why?

Detecting that a test is flaky is mostly solved; several tools do it reliably across reruns. Diagnosing the specific root cause is not. LLMs evaluated on real-world flaky test datasets achieve F1 scores above 0.88 for detection but drop below 0.57 on the same inputs when asked to identify root cause. The few automated repair tools only handle order-dependent or implementation-dependent flakiness and fail on resource contention, async timing, and environment drift. Developers spend hours reading logs and adding print statements, then watch the failure refuse to reproduce locally.