Adding ChatGPT to an application does not automatically make it AI-native. A customer support platform might use an LLM to suggest replies. A CRM might generate email summaries. Both have AI capabilities, but their core workflows can still operate without them. Now consider an application where AI interprets requests, retrieves business information, coordinates tools, and helps users complete complex tasks. That requires a different engineering approach. A truly AI-native application is designed around AI as a core capability, supported by reliable data systems, model orchestration, evaluation, security, and production infrastructure. The distinction matters because a successful prototype does not guarantee a dependable production product. Here are eight technical requirements engineering teams should evaluate before calling an application AI-native. What Does AI-Native Mean in Software Development? An AI-native application is software in which artificial intelligence plays a cen...