1. A computer-implemented process for maximizing task completion rates, comprising: using a computer to perform process actions for: receiving specifications for multiple different tasks, each task having a plurality of task contexts from one or more task publishers; receiving a separate machine-learned predictive worker model for each of a plurality of non-human virtual workers in a worker pool; receiving one or more current contexts for each of the non-human virtual workers in the worker pool; jointly evaluating all of the multiple different tasks, the task contexts, the worker models, and the current contexts to construct a plurality of optimized bundles of one or more of the multiple different tasks, the plurality of bundles together including all of the multiple different tasks; the joint evaluation further comprising formulating the combination of the multiple different tasks, the task contexts, the worker models, and the current contexts as a submodular and monotonic objective function and solving that objective function as a constrained optimization problem to compute a single predicted task completion rate that covers all of the multiple different tasks; and automatically recommending each of the bundles to particular non-human virtual workers from the worker pool, such that the single predicted task completion rate over all of the multiple different tasks is maximized by the bundle construction and task recommendation.