Why your agents need decision traces, not just documents — Zach Blumenfeld, Neo4j
May 29, 2026 · 20:12
Zach Blumenfeld from Neo4j argues that context graphs—a graph-based layer storing decision traces, precedence, and causal chains—extend standard RAG to make AI agents more accurate and explainable. A financial analyst agent, for example, can reject or accept a request by retrieving past decision traces and structurally similar precedents via graph embeddings, not just semantic similarity. Neo4j's one-command tool `uvx create-context-graph` scaffolds a full-stack app with backend, frontend, demo data, and MCP server, supporting 22 built-in domains or custom ontology generation. The underlying `neo4j-agent-memory` package handles entity extraction through a spaCy-to-GLiNER-to-LLM pipeline with deduplication and integrates with pydantic AI, LangGraph, Crew, Google ADK, and others. The episode also covers data connectors for GitHub, Notion, Jira, and Slack, and the ability to store and query decision traces with timestamps, though automated trace quality evaluation is still evolving.
