Enterprise AI

← Series Hub | Next Chapter: Part 1: Swarm Topologies → Answer-first: Production multi-agent systems succeed by encapsulating probabilistic LLM inference within deterministic software architecture guardrails: typed contracts, structured memory hierarchies, idempotent tools, and automated kill-switches.

GraphRAG vs Naive RAG: Enterprise Architecture Guide Answer-first: GraphRAG outperforms naive RAG in enterprise applications by combining knowledge graph entity extraction with vector search, resolving complex multi-hop relationship queries accurately. Most RAG (Retrieval-Augmented Generation) implementations look the same: chunk documents, embed them into vectors, store them in a vector database, retrieve by cosine similarity, and inject the top-K chunks into the LLM context. This works for simple document Q&A. It fails systematically for enterprise knowledge bases where the answer to a question depends not on a single document chunk, but on the relationships between dozens of interconnected entities. ...