LLM Agents Orchestrate 5 Data Stores for Enterprise Analytics, Cutting Synthesis to Seconds
Updated
Updated · O'Reilly Media · Sep 14
LLM Agents Orchestrate 5 Data Stores for Enterprise Analytics, Cutting Synthesis to Seconds
3 articles · Updated · O'Reilly Media · Sep 14
Summary
A new reference architecture shifts enterprise analytics from centralizing data to orchestrating access across specialized systems, letting LLM agents answer cross-company questions that older BI tools and semantic layers could not.
Three changes drive the shift: LLMs infer intent instead of relying on templates, decompose novel questions into store-specific subqueries at inference time, and synthesize results with source attribution rather than manual analyst consolidation.
The design keeps data in place across five store types—warehouse, graph, key-value, vector, and external APIs—while an orchestration layer runs queries in parallel and makes model inference, not retrieval, the main latency bottleneck.
Security and trust are built into the flow: row-level permissions are enforced before retrieval, and GraphRAG uses explicit graph paths to produce auditable reasoning chains and reduce hallucination risk.
The article argues the biggest payoff comes where analysts now spend hours stitching together three systems, with success measured by accuracy, latency, adoption, and reduced manual synthesis time.