"We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible. We told ourselves that persistence was a solved problem and began to decouple everything. If you needed search, you bolted on a search system. Ditto for caching (grab a cache), documents (use a document store), relationships (add a graph database), etc."
"In an AI-infused application, the database stops being a passive store of record and becomes the active boundary between a probabilistic model and your system of record. The difference between a cool demo and a mission-critical system is not usually the large language model (LLM). It is the context you can retrieve, the consistency of that context, and the speed at which you can assemble it."
Developers abstracted databases behind ORMs and APIs, treating persistence as an implementation detail while storing vast amounts of data. Systems added caches, search clusters, stream processors, document stores, and graph databases, shifting complexity into glue code and operational overhead. AI transforms the database into an active boundary between probabilistic models and the system of record, making context retrieval, context consistency, and assembly speed critical for reliability. AI memory becomes a database problem where inconsistency leads to hallucinations. Reliable AI agents require dependable data infrastructure that delivers consistent, retrievable context at low latency to make models production-ready.
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