  The theoretical properties of active inference agents are impressive, but how
do we realize effective agents in working hardware and software on edge
devices? This is an interesting problem because the computational load for
policy exploration explodes exponentially, while the computational resources
are very limited for edge devices. In this paper, we discuss the necessary
features for a software toolbox that supports a competent non-expert engineer
to develop working active inference agents. We introduce a toolbox-in-progress
that aims to accelerate the democratization of active inference agents in a
similar way as TensorFlow propelled applications of deep learning technology.
