The field of artificial intelligence is witnessing significant developments in agent safety and multi-agent reasoning. Researchers are focusing on evaluating and improving the safety of autonomous agents, particularly in multi-agent ecosystems where interactions between agents can expose new attack surfaces. A key challenge is ensuring that agents can communicate effectively while protecting user privacy and security. Recent studies have highlighted the importance of considering intent concealment and task complexity when evaluating agent safety, as these factors can significantly impact an agent's ability to make safe decisions. Moreover, researchers are exploring new frameworks for multi-agent reasoning, including the use of recursive refinement and incremental search to enhance the reasoning capabilities of large language models. Noteworthy papers in this area include ConVerse, which introduces a dynamic benchmark for evaluating privacy and security risks in agent-agent interactions, and AudAgent, which provides a visual framework for automated auditing of privacy policy compliance in AI agents. Additionally, the paper on Can LLM Agents Really Debate? offers valuable insights into the effectiveness of multi-agent debate in improving the reasoning performance of large language models.