The architecture of Local Digital Twins (LDTs) presents challenges through their integration of diverse data and AI insights into decision-making processes. Poor design can result in vendor lock-in, lack of interoperability, and high costs. Thus, software for LDTs must prioritize interoperability, human-centric design, and scalable solutions to maintain relevance over time. The solution proposed centers on integrating scalable, modular, and API-based architectures within LDTs. This involves using microservices for fl exibility, event-driven design for real-time responsiveness, and a hybrid cloud-edge storage strategy for optimized data management. These approaches ensure LDT systems can grow with urban demands while maintaining effi ciency and adaptability. The learning unit covers software design principles crucial for building adaptive LDT architectures. It emphasizes implementing modular systems using microservices and APIs, event-driven data processing for live interactions, and hybrid storage models. Additionally, it highlights the importance of AI governance to uphold transparency and ethical standards, ensuring LDT systems are fair, accountable, and future-ready.