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Agentic AI

The knowledge layer for enterprise AI

A manifesto for context-rich enterprise AI

Chart of the knowledge layer comprising ontologies, data, and memory

Independent study: GraphRAG makes AI agents 80% more truthful

Illustration representing a context graph for AI agent memory

Agentic AI

What is a context graph?

Welcome, GraphAware

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Emil Eifrem

CEO & Founder, Neo4j

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Sudhir Hasbe

Chief Product Officer, Neo4j

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Philip Rathle

Chief Technology Officer, Neo4j

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Most recent

Batching like a pro

Batching like a pro

When your agents share a brain: Building multi-agent memory with Neo4j

When your agents share a brain: Building multi-agent memory with Neo4j

From recall to reasoning: How context graphs upgrade an agent’s brain

Expensive AI models may sound smart, but without context, they don’t actually understand your business. It’s Ferris Bueller’s Ferrari with nowhere to drive. All engine. No roads.

The context gap: Why your smart-sounding AI struggles to reason

From data to intelligence: Why every enterprise needs an AI knowledge layer

Using agents to secure satellites’ supply chain systems

Using agents to secure satellites’ supply chain systems

Why machines need embeddings: Turning graph structure into features

Building an AI agent with memory: Microsoft Agent Framework + Neo4j

Developer

Building an AI agent with memory: Microsoft Agent Framework + Neo4j

Finding hidden bottlenecks in flight networks with Aura graph analytics on Databricks

Find impactful graph-powered insights in Databricks

Graph database

A complete guide to production-grade PrivateLink setup for Neo4j Enterprise Edition

The efficiency of on-demand graph analytics.

The persistence tax: Why it’s time to let your graph analytics sleep