Imagine an AI agent navigating a complex, unfamiliar world. Instead of blindly following pre-programmed instructions or getting stuck in a loop, it pauses, reflects on its own abilities, and chooses the best course of action. This isn't science fiction – it's the promise of metacognition in AI, and it's the driving force behind a groundbreaking new framework called MUSE (Metacognition for Unknown Situations and Environments). Humans rely on metacognition – the ability to think about our own thinking – to adapt to novel situations. We assess our understanding, choose appropriate strategies, and learn from our mistakes. Current AI systems, however, often struggle when faced with the unexpected. They lack the self-awareness to recognize their limitations and the flexibility to adjust their approach. MUSE aims to bridge this gap by equipping AI agents with metacognitive capabilities. The framework focuses on two key aspects: **competence awareness** (knowing what you know) and **strategy selection** (choosing the best approach). Researchers have developed two MUSE implementations. One uses a world model to predict the likelihood of success for different action plans. The other leverages the power of large language models (LLMs) to reason through complex scenarios and evaluate potential outcomes. Both approaches empower the agent to continuously assess its competence and iteratively refine its strategy. The results are impressive. In simulated robotic manipulation tasks and text-based game environments, MUSE agents consistently outperform traditional AI approaches, showcasing superior adaptability and problem-solving skills. They avoid getting trapped in unproductive loops, explore more effectively, and learn more efficiently from their experiences. MUSE represents a significant step forward in AI research. By incorporating metacognitive principles, we can create AI systems that are not only intelligent but also adaptable, resilient, and capable of thriving in the ever-changing real world. The challenges ahead include scaling MUSE to more complex domains like autonomous driving and integrating additional cognitive abilities, such as Theory of Mind. However, the potential rewards – truly autonomous agents capable of learning, adapting, and collaborating with humans – are immense.