Imagine software development where changes are effortless and code writes itself. That's the promise of Agile Model-Driven Development (AMDD), and Large Language Models (LLMs) like GPT-4 might just be the key. Traditional software development can be slow and rigid. Making changes is often like rearranging a Jenga tower—one wrong move and everything collapses. MDD aims to simplify this by using visual models as blueprints for the code. Think of it like designing a house in a 3D modeling program before laying a single brick. The problem? Translating those models into working code is complex and requires specialized code generators that need constant updating. LLMs offer a tantalizing solution. What if, instead of hand-crafting code generators, we could use an LLM to interpret the model and generate code directly? This research explores precisely that possibility using GPT-4 within an AMDD process. Researchers modeled a complex multi-agent system for an unmanned vehicle fleet (think drones and robots working together), using UML diagrams for the structure and behavior and enriching them with OCL and FIPA-ontology for precise rules and communication semantics. Then, they unleashed GPT-4 to generate the code in both Java and Python. The results? The LLM-generated code showed impressive alignment with the model's behavior, even adding some intelligent enhancements to agent interactions. While a few minor bugs were encountered (mostly missing library imports), the generated code proved surprisingly robust. A complexity analysis showed that adding more rules to the model increased code complexity, but not to unmanageable levels, hinting at the potential scalability of this approach. Challenges remain, particularly ensuring the generated code's correctness and security. But the research offers a glimpse into a future where agile development becomes truly agile, with LLMs bridging the gap between human design and functional code.