The payload: the semantic layer, memory, and the loop that fills it

Neo4j’s Meta Knowledge Graph (MKG) is presented as a harness-agnostic shared context and memory layer for AI agents that keeps enterprise data in its governed source systems while storing metadata, pointers, and learned operational knowledge in a graph. Its authored semantic layer, implemented through Neocarta connectors and MCP tools, maps diverse sources into a common database-schema-table-column model enriched with technical, business, and increasingly operational metadata, enabling agents to search by business meaning before querying underlying systems. Its earned memory layer captures session events through lifecycle hooks and processes them into append-only episodic observations, reusable per-tool query-error patterns that can provide fixes during the same failed turn, and selective durable learnings about projects or users. The design emphasizes provenance, tool-specific error handling, typed storage for structured external data, protection against prompt injection by excluding tool outputs from free-text learning extraction, automated deduplication and contradiction checks, and human review for ambiguous project memories and all user-scoped facts before they can affect a versioned agent persona. The post notes that future work will focus on higher-order consolidation, compression, and decay for project memories.

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