Agentic AI systems place new demands on data infrastructure: high write concurrency, bursty traffic, and mixed transactional retrieval workloads. This session uses OpenAI’s recent PostgreSQL scaling challenges as a case study to analyze where traditional database architectures break down, examining the operational trade offs and hidden costs that emerge when legacy systems reach their limits. We will demonstrate how to solve these bottlenecks by moving toward a natively distributed data plane, focusing on implementing horizontal write scalability, utilizing multi model access (graph, document, and vector), and serving both transactional and AI workloads from a single live dataset to avoid the architectural duct tape of traditional databases.