The field of multi-agent systems and control is rapidly advancing, with a focus on developing innovative solutions for complex problems. Recent research has explored the use of nonlinear control frameworks for steering networks of coupled oscillators, as well as the application of mean-field theory to model macroscopic agent behavior in large-scale systems. Additionally, there has been significant progress in the development of decentralized and self-organizing systems, including drone swarms and robotic teams. Noteworthy papers in this area include: Overcoming Quadratic Hardware Scaling for a Fully Connected Digital Oscillatory Neural Network, which introduces a novel hybrid architecture for digital ONNs, and GenGrid: A Generalised Distributed Experimental Environmental Grid for Swarm Robotics, which presents a comprehensive open-source platform for conducting swarm robotic experiments. These advancements have the potential to impact a wide range of fields, from neuroscience and robotics to distributed systems and environmental monitoring.