AI-native software development embeds AI models and data-first thinking into the architecture from day one, treating machine learning components as first-class citizens alongside traditional modules. It emphasizes continuous data pipelines, automated model training and validation, and observability to ensure models remain accurate and safe in production. Development workflows are tightly integrated with MLOps practices, enabling rapid iteration, feature experimentation, and reproducible deployments. As a result, products built this way adapt dynamically to user behavior, unlocking personalized experiences and new autonomous capabilities while requiring stronger governance around bias, privacy, and model risk.

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