The recent advancements in the field of artificial intelligence, particularly in the domain of Large Language Models (LLMs) and Vision Language Models (VLMs), are pushing the boundaries of what autonomous agents can achieve. The focus is increasingly on enhancing the adaptability and decision-making capabilities of these models in complex, dynamic environments. Key areas of innovation include the integration of metacognitive processes to improve self-awareness and strategy selection in novel tasks, the development of frameworks that enable agents to mentally explore and revise their beliefs about the world, and the creation of benchmarks that rigorously test the agentic reasoning abilities of LLMs and VLMs. Additionally, there is a growing emphasis on the role of human and LLM feedback in reinforcement learning to improve performance and accelerate learning. Notable advancements include the introduction of the Generative World Explorer (Genex) framework, which allows agents to mentally explore large-scale 3D worlds, and the Metacognition for Unknown Situations and Environments (MUSE) framework, which enhances agents' adaptability by integrating metacognitive processes. These developments collectively aim to bridge the gap between current AI capabilities and human-like adaptability and decision-making in complex, real-world scenarios.