The field of runtime verification and control of multi-agent systems is moving towards the development of more robust and scalable methods for ensuring safety and performance guarantees in complex systems. Recent work has focused on the use of conformal prediction and model predictive control to provide probabilistic safety guarantees in systems with uncertain and state-dependent behaviors. Additionally, there has been progress in the development of new methodologies for quantitative model checking and control synthesis, including the use of supermartingale certificates and generalized parameter lifting. These advances have the potential to improve the safety and efficiency of multi-agent systems in a variety of applications, including autonomous driving and swarm robotics. Noteworthy papers include: Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic Specifications, which proposes robust predictive runtime verification algorithms for stochastic multi-agent systems. Learning-Based Conformal Tube MPC for Safe Control in Interactive Multi-Agent Systems, which presents a learning-based framework for ensuring probabilistic safety in multi-agent systems with coupled dynamics.