The 35th annual conference on Astronomical Data Analysis Software & Systems 2025 in Görlitz, Germany

High-energy gamma-ray observations provide a unique window into the most extreme astrophysical environments, where particles are accelerated to relativistic energies. The \textit{Fermi} Large Area Telescope (LAT) has established blazars as the dominant class of extragalactic gamma-ray sources, exhibiting pronounced variability that encodes essential information on emission mechanisms and particle acceleration processes. In the era of Time-Domain and Multi-Messenger Astrophysics, the rapid identification and characterization of flaring sources are critical to enable prompt, coordinated multi-wavelength and multi-messenger follow-up observations.
We present \textbf{ELMA} (\textbf{E}xtended \textbf{L}AT \textbf{M}onitoring of \textbf{A}GNs), a fully automated, FAIR-compliant framework designed for the systematic extraction, analysis, and dissemination of time-dependent \textbf{LAT} data. Employing an aperture photometry approach, ELMA provides rapid, background-independent flux estimates and generates real-time alerts for a predefined list of active galactic nuclei. The pipeline produces a comprehensive suite of primary and derived data products--including light curves, spectral energy distributions, sky maps, variability metrics, and high-energy photon analyses--all accessible through a dedicated web interface and preserved within a long-term archival system.
The framework is optimized for scalability, transparency, and reproducibility, enabling sustained, large-scale monitoring of $\gamma$-ray active galactic nuclei. Planned developments include the integration of machine learning--based variability detection, Bayesian uncertainty quantification, and an AI-driven agent for autonomous pipeline management. These enhancements will support intelligent, real-time operations within the broader landscape of multi-messenger astrophysics.