  AGILE is an ASI space mission launched in 2007 to study X-ray and gamma-ray
phenomena in the energy range from $\sim20$ keV to $\sim10$ GeV. The AGILE Team
developed a real-time analysis pipeline for the fast detection of transient
sources, and the follow-up of external science alerts received through networks
such as the General Coordinates Network. We developed a new Deep Learning
method for detecting and localizing Gamma-Ray Bursts (GRB) in the AGILE/GRID
sky maps. We trained the model using sky maps with GRBs simulated in a radius
of 20 degrees from the center of the map, which is larger than 99.5 \% of the
error region present in the GRBWeb catalog. We also plan to apply this method
to search for counterparts of gravitational wave events, which typically have a
wider localization error region. The method comprises two Deep Learning models
implemented with two Convolutional Neural Networks. The first model detects and
filters sky maps containing a GRB, while the second model localizes its
position. We trained and tested the models using simulated data. The detection
model achieves an accuracy of 95.7 \%, and the localization model has a mean
error lower than 0.8 degrees. We configured a Docker container with all the
required software for data simulation and deployed it using the Amazon Web
Service to calculate the p-value distribution under different conditions. With
the p-value distribution, we can calculate the statistical significance of a
detection.
