Digital-native startups ditch rigid databases for agentic stacks with MongoDB

Presented by MongoDB The 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. Modelence: Building the agent-native cloud Modelence 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 Modelence. “Live data streams, vector search, all as part of the main database. For AI agents, it’s especially important to have a single platform where everything can be done, because connecting multiple platforms together makes it more error prone.” Modelence standardized on MongoDB Atlas because its document model aligns with how AI agents process and generate data, allowing schemas to evolve rapidly without manual migrations.

The platform pairs that flexibility with a typed schema layer on top, a deliberate architectural decision. “MongoDB’s document model enables us to both keep things simple and at the same time decide how structured we want everything to be,” Shatakhtsyan says. “We still add a typed schema on top, which tremendously improves the accuracy at which AI can generate fully working, reliable web apps.” The TypeScript integration has been especially consequential, he adds.

“Because MongoDB types and values can be directly translated to TypeScript, it becomes an extension of the Modelence framework and our App Builder has a single source of truth for both app logic and database,” Shatakhtsyan explains. The result is a platform that can move from planning to a running live feature in minutes with significantly fewer regressions. That speed and reliability helped Modelence raise $3 million in seed funding and successfully launch an AI-native app builder that handles the entire application lifecycle end-to-end.