Personnel selection and agent readiness are critical priorities for defense organizations, particularly in ensuring staff are adequately prepared for high-stakes environments such as public safety and military operations. Readiness assessments evaluate cognitive abilities, mental resilience, physical fitness, decision-making skills, and the ability to manage stress and workload. Traditional methods rely on performance indices from psychometric tests and physical evaluations. However, research suggests that incorporating behavioral and neurophysiological data, like cerebral blood flow oxygenation, enhances predictive capabilities for agent readiness. Methods such as the Revised Multi-Attribute Task Battery (MATB-II) have been developed to assess human decision-making and complex problem-solving capabilities, which are skills often considered when assessing readiness. The SHAD (Sensing Humans for Augmented Debrief) system is a training-support interface developed for such a multimodal approach that integrates wearable technology with psychometric profiling. SHAD captures neurophysiological signals—such as heart rate variability, brain oxygenation, and respiratory data—alongside psychometric assessments to predict and evaluate agents’ mental and physical states in real time. The experimental design used MATB-II as a task to induce stress and workload in 41 participants from two public safety organizations. A machine learning model enabled readiness predictions, offering insights for personnel selection, mission preparation, and adaptive training. The model was trained on both psychometric data and neurophysiological data collected during the MATB-II task. This solution could significantly enhance public safety and defense missions by optimizing agent performance.