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Neo4j announced the Neo4j Context Engine today, a product built to give AI agents a shared, reusable knowledge layer for reasoning over enterprise data. The problem it targets is one most agent deployments hit early: an agent does not know what “customer,” “account,” “supplier,” or “risk” means inside a particular company, because that meaning is scattered across databases, applications, documentation, and individual AI implementations. The Context Engine is available to enterprises now, and Neo4j is taking applications for design partners. “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 chief product officer at Neo4j. 6-Layer Business Ontology The Context Engine centers on a predefined 6-layer graph model that maps an organization, its business processes, its data, and its AI assets into Neo4j. Neo4j built the model from patterns across hundreds of enterprise customer deployments. The ontology connects physical data sources directly to business logic, so AI and other systems can make decisions with the surrounding context attached, and it serves both design-time architecture work and run-time execution. The Context Build and Runtime component builds the ontology from existing data sources such as Databricks and Snowflake, and exposes it to AI orchestration layers that drive process-guided agents or conversational analytics agents. Teams create and operate the semantic layer through headless execution, command-line tools, or Python packages. It works across existing data platforms, data catalogs, and enterprise storage systems, carries built-in change management, and uses GraphRAG retrievers to pull context out of the semantic layer. Context Studio Governance and Persistent Memory Layer Context Studio is the control plane for the ontology. It gives teams one place to manage and monitor the environment, a visual builder for the business ontology and the workflows that run on it, and setup tooling for AI agents, Model Context Protocol (MCP) servers, and developer tools. Context Memory is a graph-native memory layer that gives agents persistent, structured memory across short-term, long-term, and reasoning memory, along with skill distillation. It logs agent reasoning and operational paths as a permanent record of enterprise experience, runs graph algorithms over past interactions to condense repeated events into reusable knowledge, and feeds what it learns back into the core system to improve agent reasoning and accuracy over time. Neo4j frames the launch around a third consumer of enterprise data. Humans have understood the relationships between data intuitively and institutionally, and applications have done the same through hardcoded logic. AI agents are the third consumer, and the company’s argument is that inferring relationships is no longer enough as agentic use cases grow more complex; the relationships have to be represented explicitly, which is what a graph does natively.
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