The use of additive manufacturing (AM) for production has increased due to no geometrical constraints, no need for complex assemblies, and fast iteration that delivers additional benefits. There are still many challenges that prevent AM to be utilized in a reliable and cost-efficient production scenario: • How to reduce manual work, especially in post-processing? • How about automation in general? • How to detect errors while printing? This presentation will examine the current state of automation and error detection in AM. Industrial robots have been adopted in production plants for quite some decades now, but there aren't many in AM. This session will evaluate various approaches to how production data, CAD or Gcode, can automatically generate trajectories for autonomous post-processing of AM objects. The earlier a possible error is detected, the more time and material can be saved. Artificial intelligence for in-process error detection could save the costly quality assurance after completion of a print.