We are currently moving from "AI-enabled" systems, where artificial intelligence is an additive feature, to "AI-native" systems, where intelligence is the foundational, architectural core. An AI-native application is not merely a traditional application with a machine learning model bolted on; it is an entirely new class of software designed from the ground up to learn, adapt, and act autonomously. These systems are architected around continuous data ingestion, real-time model interaction, and a contextual understanding of the application and runtime environment.

We'll discuss the key elements mentioned above and the difference between existing applications that have added AI capability as an accessory, and this new class of applications that are built with AI in mind from the start. While we'll use the Java ecosystem in examples, the principles we'll discuss are language agnostic. We'll focus on the architecture discuss these topics:

* Big Data and event streams
* Integration with other services and APIs and building knowledge graphs as a model for long-term app viability
* Testing and model migration
* Scaling the usage of these apps