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Particle Physics and Machine Learning are a match made in heaven. Due to the extreme gap between length and energy scales of the physics we measure and the physics we want to learn about, we rely since decades on advanced computer-aided pattern recognition algorithms to learn about nature. The last one and a half decades have brought about a massive expansion of the capabilities of such algorithms through the use of machine learning which allows us to push far beyond the original scope of large collider experiments such as the LHC. But machines not only allow us to do analyses better but also faster through the use of automation, which recently has received renewed attention based on Agentic AI systsems. In this talk I will review some recent developments in the use of ML and AI in the field of collider physics.