Digital-native startups are ditching rigid databases for their agentic stacks
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. Their experiences define what an agent-native data stack looks like in production, and why using Atlas enables...
Copyright of this story solely belongs to venturebeat.com. To see the full text click HERE
