I run a robotics company and we tend to do the same, namely using agent terminology. This does indeed not preclude new methods. SLAM is of course what everybod

I run a robotics company and we tend to do the same, namely using agent terminology. This does indeed not preclude new methods. SLAM is of course what everybody uses in autonomous robotics. But you also have reinforcement learning and imitation learning and a wide diversity of representations and models from DEC-POMDPs to Bayesian models to neural nets. Each time you'll need somehow to decompose into modules, units, etc. Agents represent something in the real world and are often the right term for a module that has (virtual) sensors and actuators. Apart from that I would recommend to focus on something, so you can really excel in that part of AI. We have chosen "understanding where an AI is located in the real world" as our main goal. I can imagine there is something similar you would like to achieve for AI. Just my two cents! Good luck!!!