Complex sensor systems available on Naval Unmanned Air System (UAS) platforms requires advanced techniques to enhance their resiliency and survivability. This includes autonomous artificial intelligence (AI) architectures that can process, analyze, and provide actionable intelligence in terms of understanding and reporting on adversarial actions/events focused on damaging/crippling Navy UAS system. This includes system failures, either through degradation over time or operational mistakes. Unmanned vehicles must operate in unstructured environments that are inherently unpredictable and dynamical. An autonomous UAS must have some degree of cognitive intelligence in order to undertake tasks without direct and continuous human involvement, especially in unknown environments. Colorado Engineering Inc. and ISEA TEK LLC, the CEI Team, proposes research into AI-enabled cognitive machine learning system for Real-time Autonomous Sensor Processing (RASP) technologies that will provide Unmanned Service Systems (USS) and Unmanned Air Systems (UAS) with the capabilities required for semi-supervised and autonomous command and control. This research will create the RASP system, utilizing an intelligent multi-agent processing infrastructure that will meet the Navys needs now and into the future. The focus of this research is to create AI-enabled technologies that provide complete sensor and technology integration for effective UAS mission.