The TP Blackboard model uses Tensor Product Representations (TPR’s) to represent a neural blackboard, which neural agents then use to communicate, coordinate, and compose their results of transforming an input structure (tree) into the labeled output structure (tree). A variety of neural agent types and tree-encoding techniques are explored. One of the surprising results is that simple MLP agents are able to learn complex transformations between TPR-encoded input/output trees.