  Game agents such as opponents, non-player characters, and teammates are
central to player experiences in many modern games. As the landscape of AI
techniques used in the games industry evolves to adopt machine learning (ML)
more widely, it is vital that the research community learn from the best
practices cultivated within the industry over decades creating agents. However,
although commercial game agent creation pipelines are more mature than those
based on ML, opportunities for improvement still abound. As a foundation for
shared progress identifying research opportunities between researchers and
practitioners, we interviewed seventeen game agent creators from AAA studios,
indie studios, and industrial research labs about the challenges they
experienced with their professional workflows. Our study revealed several open
challenges ranging from design to implementation and evaluation. We compare
with literature from the research community that address the challenges
identified and conclude by highlighting promising directions for future
research supporting agent creation in the games industry.
