The field of Artificial Intelligence (AI) is moving towards the development of more complex and adaptive systems, with a focus on taming uncertainty and enabling self-improving AI. This is being achieved through the introduction of automation and observability frameworks, such as those that observe, analyze, and optimize agentic AI systems. Another key area of research is the optimization of resource allocation in edge-cloud systems, with a focus on sustainable, explainable, and maintainable automation. Noteworthy papers in this area include the introduction of AgentOps, a comprehensive framework for observing, analyzing, optimizing, and automating operation of agentic AI systems, and ARRC, a recommender system that delivers explainable, cross-layer resource recommendations directly into operator workflows. A Survey of AIOps in the Era of Large Language Models provides a comprehensive understanding of the impact, potential, and limitations of large language models in AIOps, highlighting the state-of-the-art advancements and trends in this area.