Modern Data Management Methods for Vector Databases and Multi-Model Datasets in Large-Scale Agentic Artificial Intelligence Enterprise Systems

Abstract

Large scale agentic artificial intelligence systems retrieve evidence from vector databases that store dense embeddings alongside structured, numeric, and temporal attributes, and they turn that evidence into business decisions through repeated reasoning loops. Managing this multi-model data at enterprise scale raises three coupled questions: how to index billions of vectors so that retrieval is both accurate and fast, how to store them compactly enough to fit memory budgets, and how to engineer the decision loop so that it converges rather than drifts. This paper gives a unified mathematical and algorithmic treatment of these questions and introduces a proposed architecture, the Multi-Model Vector Loop Engine, that couples a quantized vector index with a feedback controlled agentic decision loop. We formalize approximate nearest neighbour retrieval, product quantization, hybrid multi-model filtering, and the decision loop as a fixed point iteration, and we prove that under a contraction condition the loop converges geometrically. We implement the full stack from first principles and report reproducible measurements on one hundred thousand 128-dimensional embeddings. An inverted file index raises recall at ten from 0.546 at a single probe to 1.000 at thirty two probes, product quantization trades memory for accuracy from recall 0.258 at 128 times compression to 0.701 at 16 times compression, a hybrid query with 4.0 percent selectivity resolves in 0.043 milliseconds, and the agentic loop converges to its fixed point within three iterations while raising cumulative coverage and modelled decision utility. A single ingestion shard encodes about 48800 records per second, so linear sharding reaches the five million records per second target with 103 shards. The results give practitioners a rigorous basis for managing vector and multi-model data under the accuracy, memory, and decision quality constraints of agentic enterprise systems.