Knowledge Graph Architectures for Enterprise AgentsDraft

Flat Retrieval-Augmented Generation works for demos and small document sets. It breaks in production. When every chunk competes equally in a single vector space, retrieval precision degrades as the corpus grows, cross-document reasoning becomes impossible, and there's no natural place to enforce access control. The answer isn't better embeddings — it's better architecture.

This paper starts with the insight that every production knowledge system uses graphs internally — hierarchy is an emergent property, not a structural constraint. It presents the enterprise knowledge graph model where purpose-typed nodes have independent access configurations, connected by organizational, topical, and access edges. Five implementation patterns show how to build this in practice.

What You'll Learn

• Why flat RAG precision degrades predictably as corpus size grows, and the specific failure modes in enterprise settings
• The enterprise knowledge graph model: purpose-typed nodes with node-level access control, connected by organizational and cross-cutting edges
• How ReBAC + ABAC enables per-node access configuration — governance using the same relationship model orgs already use for people
• Five implementation patterns (tree summarization, community detection, semantic layers, domain agents, multi-level graphs) with concrete trade-offs
• Where to enforce permissions in the retrieval pipeline: pre-retrieval filtering as the preferred path, post-retrieval as a fallback, and why generation-time redaction is not a control
• Why provenance is non-negotiable: structured references from every derived artifact back to its source facts, enabling self-correction and auditability
• How knowledge builders from the data pipeline layer produce nodes and edges in the knowledge graph

Who This Is For: ML engineers, data architects, and platform teams building RAG systems that need to scale beyond a single-team knowledge base.