Large Language Models (LLMs) like GPT-4 have revolutionized how we interact with AI, allowing us to give instructions in natural language and receive impressively human-like responses. But what happens when those instructions get complex? Current LLMs often stumble with multi-step commands or struggle to adapt to new situations. They might plan a trip that includes non-existent flights or fail to adjust to unexpected delays. Why? Because they lack a key ingredient: real-world grounding. They excel at generating text but struggle to connect their words to practical actions. This is where neurosymbolic AI comes in. This hybrid approach combines the strengths of neural networks (pattern recognition, flexibility) with symbolic reasoning (logic, rules). Imagine a system that not only understands your instructions but also breaks them down into manageable steps, considers real-world constraints, and adapts to changing circumstances. Researchers are developing a neurosymbolic framework with three key components: a Symbolic Task Planner that deconstructs complex instructions, a Neural Semantic Parser that translates those subtasks into executable actions, and a Neurosymbolic Executor that implements the actions while dynamically updating its understanding of the situation. This means an AI travel planner that can book your flights, adjust to flight cancellations, and suggest alternate activities based on weather conditions, all while keeping your preferences in mind. This approach has exciting implications beyond travel planning, improving instruction following in robotics, healthcare, and other fields. Neurosymbolic AI offers a promising path towards building more reliable, adaptable, and ultimately, more useful AI systems.