[Context] The dynamic nature of machine learning (ML) development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management. Agile approaches, with their flexibility and incremental delivery, seem well-suited to address this dynamism. However, it is unclear how to effectively apply these methods in the context of ML-enabled systems. [Goal] Our goal is to outline the state of the art in agile management for ML-enabled systems. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with backward and forward snowballing iterations. [Results] Our study identified 27 papers published between 2008 and 2024. From these, we identified eight approaches, 31 adapted practices, categorized recommendations into eight key themes, and identified main challenges. [Conclusion] This study contributes by mapping the state of the art of agile management for ML.