The field of autonomous system optimization and security is rapidly evolving, with a focus on developing innovative solutions that leverage Large Language Models (LLMs) and agentic frameworks. Recent research has explored the application of LLMs to optimize system performance, improve security, and enhance decision-making capabilities. Notable advancements include the development of frameworks that enable autonomous optimization of Linux schedulers, end-to-end Kubernetes management, and threat modeling for public safety systems. These solutions have demonstrated significant improvements in performance, cost reduction, and security. Furthermore, researchers have emphasized the importance of evaluating model- and agentic-level vulnerabilities in LLMs, highlighting the need for standardized methodologies and empirical validation. Overall, the field is moving towards creating more resilient, trustworthy, and autonomous systems that can operate efficiently and effectively in complex environments. Noteworthy papers include: SchedCP, which achieves up to 1.79x performance improvement and 13x cost reduction; KubeIntellect, which supports natural language interaction across the full spectrum of Kubernetes API operations; and ThreatGPT, which enhances public safety through threat modeling and analysis.