Graph engineering is missing a graph

Graph engineering should encompass both the execution graph that coordinates agent tasks, routing, verification, and budgets, and the context graph that represents durable knowledge such as entities, relationships, provenance, permissions, and temporal facts. The piece argues that many agent systems improve orchestration while still relying on fragmented vector stores, document databases, relational systems, and session memory, creating inconsistent context, costly over-retrieval, and weak auditability. It distinguishes this broader architecture from GraphRAG, which it presents as a retrieval technique rather than a complete system design, and recommends integrating graph traversal, vector similarity, full-text search, filtering, permissions, and time constraints in a single query layer. It also emphasizes schema design, vocabulary reuse, entity resolution, fact expiration, transactional write-back, and separate handling of event and mention times to prevent context graphs from becoming unreliable collections of extracted triples. For enterprise use cases, the author highlights shared context, auditable retrieval, provenance, temporal truth, and data-level access controls, and presents SurrealDB and its Spectron memory layer as a multi-model approach intended to unify these capabilities beneath agent orchestration frameworks.

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