AI Network for Training, Inference, and Agentic Interactions

Artificial Intelligence (AI) is rapidly transforming industries and everyday life, fueled by advances in model architectures, training paradigms, and data infrastructure for generation and consumption. However, the effectiveness and reliability of AI depend on two foundational processes: training and inference. Each process introduces unique challenges related to data management, computation, connectivity, privacy, trust, security, and governance. In this draft, we introduce the Data and Agent Aware-Inference and Training Network (DA-ITN)—a unified, intelligent, multi-plane framework designed to address the full spectrum of requirements needed to enable various services and interactions within the AI ecosystem. DA-ITN provides a scalable and adaptive infrastructure that connects AI clients, data providers, model providers, agent providers, service facilitators, and computational resources to support end-to-end training, inference, and agentic interaction lifecycle operations. The architecture features dedicated control, data, and operations & management (OAM) planes to ensure reliability, transparency, and accountability between interacting parties. The proposed framework is not intended for end-to-end standardization, but is intended to serve as a reference framework for the AI ecosystem of the future. Various protocols for the different building blocks shall be defined to enable different functionalities.