Neo4j has launched the Neo4j Context Engine, a platform designed to help enterprises build a shared knowledge layer that enables AI agents to interpret business data, understand relationships across systems and deliver more reliable, explainable outputs.
Available in private preview, the Context Engine brings together business ontologies, context-building and runtime capabilities, visual management tools, and graph-native memory. The platform aims to help organisations make enterprise knowledge reusable across AI agents, rather than configuring context separately for individual applications.
As enterprises deploy AI agents to automate workflows, a key challenge is ensuring that these systems understand the meaning of business concepts and the relationships between them. Information about customers, accounts, suppliers and risks is often distributed across databases, applications, documentation and individual AI implementations.
Neo4j said the Context Engine is designed to address this fragmentation by connecting underlying data sources with business logic, giving AI agents access to structured context when making decisions or executing tasks.
Building a shared context layer
The launch aligns with the emerging focus on AI context platforms, which aim to organise and govern enterprise knowledge for use across AI applications.
In its August 2026 Market Overview for AI Context Platforms, Gartner described these platforms as software that enables data and AI engineering teams to construct, govern and deliver semantically organised knowledge to AI agents and other AI applications. The report highlights the importance of treating context as an enterprise asset, separate from the lifecycle of individual agents.
Sanjeev Mohan, principal at SanjMo, said context is essential to making enterprise AI reliable but requires more than adding data to a model.
“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,” Mohan said. He added that organisations need to represent relationships explicitly as agentic AI applications become more complex.
Sudhir Hasbe, president of technology and chief product officer at Neo4j, said the ability to model relationships and explain why they exist is central to addressing the context challenge. He said the Context Engine brings the company’s experience with graphs and ontologies into a platform that enterprises can use to develop context-aware AI applications.
Four components underpin the platform
The Neo4j Context Engine combines four main components to help enterprises build, manage and use business context.
Business Ontology provides a predefined six-layer graph model for mapping an organisation’s business processes, data and AI assets. According to Neo4j, the model draws on practices from hundreds of enterprise deployments and connects physical data sources with business logic. It is intended to support both architecture design and runtime execution.
Context Build and Runtime enables enterprises to construct the business ontology using existing data sources, including platforms such as Databricks and Snowflake. AI orchestration layers can then access the ontology to support process-guided agents and conversational analytics. The component also supports management through command-line tools and Python packages, connectivity across enterprise data platforms, and change management. Graph retrieval-augmented generation (GraphRAG) retrievers are used to retrieve relevant context from the semantic layer.
Context Studio provides a central interface for managing and governing the business ontology. It includes visual tools for defining ontologies and workflows, monitoring the environment, and configuring AI agents, Model Context Protocol (MCP) servers and developer tools.
Memory and Learning adds a graph-native memory layer intended to help agents retain information from previous decisions and interactions. It supports short-term, long-term and reasoning memory, alongside skill distillation. The system can track agent reasoning and operational paths, use graph algorithms to identify recurring patterns, and feed distilled knowledge back into the system to improve future responses.
Governance and explainability in focus
The platform’s emphasis on reusable context and memory reflects a broader enterprise requirement: ensuring AI agents can operate across systems without losing the business context needed to make appropriate decisions.
By centralising the modelling and management of business knowledge, Neo4j aims to help organisations reduce fragmented context configurations and improve the consistency of AI-driven workflows. The company also positions governance, customisation and change management as core capabilities for enterprises operating complex data environments.
The Context Engine is currently available in private preview. Neo4j is inviting organisations to participate as design partners as it develops the offering.