Advances in AI-driven cognitive systems are transforming the field of radar and electronic warfare by enabling adaptive, real-time countermeasures against mode-agile threats. Traditional static library systems are ineffective against these threats, which can deploy unexpected frequencies, modulation techniques, and hopping schemes, rendering legacy electronic protect, attack, and support systems unable to respond1. AI/ML cognitive architectures can power cognitive radar/EW systems, allowing them to learn and adapt in real-time, making them more effective against these dynamic threats. The shift from criminal to geopolitical threat models, particularly with state-aligned activity, requires a different approach to electronic warfare. As a result, practitioners must reconsider their strategies to counter these emerging threats, leveraging AI/ML techniques to stay ahead of adversaries. The integration of AI-driven cognitive systems in radar and electronic warfare is crucial for developing effective countermeasures against mode-agile threats.