In 2019, researchers at DeepMind published a suite of reinforcement learning environments called Behavior Suite for Reinforcement Learning, or bsuite. Each environment is designed to directly test a core capability of a general reinforcement learning agent, such as its ability to generalize from past experience or handle delayed rewards. The authors claim that bsuite can be used to benchmark agents and bridge the gap between theoretical and applied reinforcement learning understanding. In this blog post, we extend their work by providing specific examples of how bsuite can address common challenges faced by reinforcement learning practitioners during the development process. Our work offers pragmatic guidance to researchers and highlights future research directions in reproducible reinforcement learning.