This paper proposes a hybrid deep learning framework for few-shot malware detection using an Autoencoder Feature Extractor and Model Agnostic Meta Learning. The model demonstrates high accuracy and robustness in adapting to limited data scenarios, but is not directly related to AI agents or their infrastructure.