For a long time, backend engineering has been divided into two camps: Relational databases (SQL) for rigid, structured tables, and Document stores (NoSQL/JSON) for flexible, unstructured data.
But when you are building for AI Agents, both of these traditional approaches introduce massive architectural friction.
AI agents don't think in rows, columns, or complex foreign key joins. They don't want to parse heavily nested, arbitrary JSON text blobs either. Agents think in state, context, and entities. They operate best when data looks exactly like the object structures they manipulate in their runtime environments.
When I designed the core primitives for thingd, I wanted the absolute speed of a local relational database combined with the flexibility of an object-first paradigm. Here is how we bypassed the typical ORM bloat and built an object-shaped memory engine natively in Rust.
Why typical DB abstractions break for Agents
