We present the Active Galactic Nuclei (AGN) classifier as currently implementedwithin the Fink broker. Features were built upon summary statistics of availablephotometric points, as well as color estimation enabled by symbolic regression. Thelearning stage includes an active learning loop, used to build an optimized trainingsample from labels reported in astronomical catalogs. Using this method to classifyreal alerts from the Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy,93.8% precision and 88.5% recall. We also describe the modifications necessary toenable processing data from the upcoming Vera C. Rubin Observatory Large Surveyof Space and Time (LSST), and apply them to the training sample of the ExtendedLSST Astronomical Time-series Classification Challenge (ELAsTiCC). Resultsshow that our designed feature space enables high performances of traditionalmachine learning algorithms in this binary classification task.