Presented by MongoDBThe gap between what AI models and agents can produce and what legacy infrastructure can reliably support is known as architectural drag, and it is the defining bottleneck of the agentic era. The data layer underneath an agentic system must handle variable schemas, vector embeddings, real-time retrieval, and multi-tenant scale, often simultaneously and without human intervention to manage migrations — but traditional relational databases weren’t natively designed for document flexibility or AI capabilities. Fixed schemas require manual updates every time an AI agent introduces a new data shape, while separate vector databases add latency and synchronization overhead.Three digital-native startups — Huntr, Modelence, and Tavily — solved this problem the same way: by building on MongoDB Atlas, a unified database platform with native vector search, hybrid search, and managed autoscaling. Their experiences define what an agent-native data stack looks like in production, and why using Atlas enables developers to easily build complex AI native companies.Modelence: Building the agent-native cloudModelence is an AI app builder with an open-source framework designed specifically for agent-native development, enabling anyone to build and deploy production-ready web applications, including APIs and databases, in minutes. The company recognized early that most backend infrastructure was built for humans, not AI, and that the rigid schema management and complex migrations of traditional systems create operational drag that causes agents to fail when trying to build production-ready apps.“Choosing MongoDB helped us keep everything in a single place, which is an important property of what we strive to do for our own users,” says Aram Shatakhtsyan, co-founder and CEO of Model …
