AI systems often become complex and difficult to manage without proper architectural discipline, leading to opaque models and cascading failures. Test-Driven Development (TDD) principles can transform these systems into robust, maintainable frameworks by prioritizing modularity, maintainability, and verifiability from inception. Unlike traditional TDD, AI-specific TDD accommodates probabilistic outputs and complex data dependencies by using statistical testing and threshold-based assertions. This approach helps AI teams design resilient systems with clearly defined performance boundaries and interface contracts, allowing components to evolve without compromising stability. Despite misconceptions that TDD is unsuitable for AI due to non-deterministic behavior, industry case studies show that well-structured TDD accelerates development and facilitates confident refactoring. Modern TDD practices utilize techniques like synthetic data generation and invariance testing to validate model behavior, even without extensive labeled datasets. Tools like Galileo assist in implementing robust TDD practices by providing automated test generation, continuous validation, scenario-based testing, and performance monitoring, thereby enhancing the quality and reliability of AI systems.