The rise of agentic AI is exposing the limitations of fragmented data architectures. Traditional systems — built for structured, transactional workloads — struggle to support the real-time, multimodal demands of various types of modern AI, including generative AI and AI agents. Multimodel data platforms (MMDPs) have been around for a while but are seeing renewed interest as a unified foundation that integrates relational, graph, document, key-value, and vector data models within a single engine. This report explores how MMDPs simplify architectures, enhance developer agility, improve governance, reduce costs, and serve the cognitive requirements to power agentic AI.