It depends on the needs of the specific application. What typically happens is that you’d use BO to globally converge to within some tolerance and use the resulting surrogate to get a map of where the interesting regions are. You can then more densely sample these candidate regions or switch to a gradient-based method (via finite difference). For uncertainty information, we usually add this as a noise parameter, either on the input samples, or as part of the GP kernel.