Neo4j, the world's leading graph intelligence platform, has announced the Neo4j Context Engine, which allows companies to rapidly create the knowledge layer, a reusable, shared substrate that agents can use to consistently reason over enterprise knowledge. Part of the graph intelligence platform, the new product creates a shared understanding of business data across systems, enabling trustworthy enterprise AI with greater meaning, reach, and memory for more reliable AI agents.
Data has always had two consumers: humans, who have institutionally and intuitively understood the relationships between data, and applications that did the same through hardcoded logic. Today, data has a third consumer, AI agents, which have the potential to massively accelerate enterprise operations through automation, but don't inherently understand what "customer," "account," "supplier," "risk," or thousands of other relationships and concepts mean within a particular organization. That meaning is often scattered across databases, applications, documentation, and individual AI implementations.
In the August 2026 Gartner Market Overview for AI Context Platforms, Gartner defines AI context platforms as software that enables data and AI engineering teams to construct, govern and deliver semantically organized knowledge to AI agents and other AI applications and services. By treating context as a first-class enterprise asset rather than an agent-by-agent configuration, these platforms separate the life cycle of organizational knowledge from the life cycle of individual agents.
"Context is what makes enterprise AI reliable and trustworthy," said Sanjeev Mohan, principal at SanjMo. "But context is not just more data, it's really about relationships: not just that two things are connected, but why and how. That is exactly what graphs model natively. As agentic use cases get more complex, inferring those relationships is not enough. Organizations need to represent those relationships explicitly, not just guess at them. Done right, that is the context layer enterprise AI runs on."
"Modeling relationships, and being able to show exactly why and how those relationships exist, is absolutely essential to solving the problem of context for AI. All the other options are a band-aid on the problem that breaks as AI agents grow in complexity and system access requirements," said Sudhir Hasbe, President of Technology and CPO at Neo4j. "Only Neo4j has decades of expertise using graphs and ontologies to solve complex business challenges. All that expertise will now be available to enterprises through the Neo4j Context Engine."
The Neo4j Context Engine brings together all of the tools and technologies needed for organizations to provide context to agents so they are accurate and explainable. It includes Business Ontology with proven design from hundreds of enterprise customer deployments, seamless connection of physical data sources to business logic, and dual-purpose utility powering both design-time architecture and active run-time execution.
Context Build and Runtime enables enterprises to build the business ontology from existing data sources like Databricks or Snowflake and enables AI orchestration layers to access the ontology to drive process-guided agents or conversational analytics agents. Broad connectivity works across existing data platforms, catalogs, and enterprise storage systems, with flexible management through headless execution, command-line tools, or Python packages.
The platform provides GraphRAG retrievers for accurate context retrieval, Context Studio for ontology management, Central Control Plane for unified monitoring, and Agent Setup to simplify configuration of AI agents and model context protocol servers. Context Memory offers a graph-native memory layer for reinforced learning from past decisions with decision tracking, knowledge distillation through graph algorithms, and continuous improvement feeding learned insights back into the system.
About Neo4j
Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph database and capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world's largest graph community.
