The field of agentic systems and multi-agent architectures is witnessing significant advancements, with a focus on autonomous decision-making, self-improvement, and adaptability. Researchers are exploring the use of large language models (LLMs) to enable agents to learn, reason, and interact with their environment in a more efficient and effective manner. The development of self-evolving multi-agent architecture search frameworks and the integration of speculative actions are also notable trends, aiming to improve the performance and scalability of agentic systems. Furthermore, the investigation of utility-learning tension in self-modifying agents and the proposal of decentralized architectures, such as swarm learning, are shedding light on the potential limitations and future directions of these systems. Noteworthy papers include: AutoMaAS, which introduces a self-evolving multi-agent architecture search framework, and Speculative Actions, which proposes a lossless framework for faster agentic systems. Additionally, Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks highlights the potential of swarm learning in dynamic environments, and A Multi-Agent Framework for Stateful Inference-Time Search presents a novel approach to stateful inference-time search.