Context Graphs

Context Graphs

Context graphs are graph-backed memory and retrieval structures that connect entities, events, policies, previous decisions, tool traces, and reasoning records so agents can make and explain decisions rather than merely retrieve documents [src-088].

Key points

  • Neo4j's late-May AI Engineer talks frame context graphs as a decision layer above ordinary RAG: they supply the facts, precedents, policies, and relationships needed to answer "what should happen and why?" [src-088].
  • A context graph can combine short-term conversation memory, long-term entity memory, and reasoning memory into one queryable structure [src-088].
  • Decision traces matter because many production questions depend on precedents, causal chains, authority, reversibility, risk, and the cost of being wrong, not only semantic similarity to a document [src-088].
  • Graph-native retrieval can mix Cypher queries, vector search, graph embeddings, and graph algorithms to find structurally similar cases as well as semantically similar text [src-088].
  • The practical promise is auditability: humans can inspect which entities, policies, previous decisions, and risk factors the agent used before accepting, rejecting, or escalating a decision [src-088].

Related entities

Related concepts

Source references

  • [src-088] AI Engineer late-May 2026 channel update (48 transcripts, 2026-05-15 to 2026-05-31)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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