Neo4jⓇ, the world’s leading graph intelligence platform, today announced the Neo4j Context Engine, which allows companies to rapidly create the knowledge layer, a re-usable, shared substrate that agents can use to consistently reason over enterprise knowledge. The Neo4j Context Engine provides 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. AI lacks the clear map and instructions that the business needs to provide the right data, in the right structure, at the right time.
In the August 2026 Gartner® Market Overview for AI Context Platforms, written by Kjell Carlsson, Afraz Jaffri, Christopher Long, and Radu Miclaus, it cites: “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, increasing the organization’s ability to scale and govern its AI agents across different domains.”*
“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, including:
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Business Ontology: predefined 6-layer graph model that enables enterprises to map their organization, business processes, data, and AI assets into Neo4j.
- Proven Design: The model has been defined using industry best practices from hundreds of enterprise customer deployments.
- Seamless Connection: Connects physical data sources directly to business logic, allowing AI and other systems to make context-aware decisions.
- Dual-Purpose Utility: Powers initial design-time architecture and active run-time execution.
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Context Build and Runtime: enables enterprises to build the business ontology from their existing data sources, like Databricks or Snowflake, and enables AI orchestration layers to access the business ontology to drive process-guided agents or conversational analytics agents.
- Flexible management: Creates and operates the semantic layer using headless execution, command-line tools, or Python packages.
- Broad connectivity: Works across existing data platforms, catalogs, and enterprise storage systems.
- Governance and customization: Built-in change management and adaptable frameworks tailored to complex enterprise requirements.
- GraphRAG: Uses GraphRAG retrievers to quickly pull accurate context from the semantic layer.
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Context Studio: provides the ability to manage and govern the business ontology.
- Central Control Plane: Provides a unified experience to manage and monitor the entire environment.
- Ontology and workflow builder: Teams can visually define business ontology and the workflows that run on it.
- Agent Setup: Simplifies the configuration of AI agents, model context protocol (MCP) servers, and developer tools.
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Context Memory: graph-native memory layer designed to give AI agents memory and reinforced learning from past decisions. It supports short-term, long-term, and reasoning memory as well as skill distillation.
- Decision Tracking: Logs agent reasoning and operational paths to form a permanent record of enterprise experience.
- Knowledge distillation: Graph algorithms analyze past interactions and condense repeated events into reusable knowledge.
- Continuous Improvement: Feeds learned insights back into the core system to refine future agent reasoning and accuracy over time.
Availability
The Neo4j Context Engine is now available in private preview. Contact us to become a designer partner.
For more information
To learn about the Neo4j Context Engine, read our blog or visit our website.
*Gartner, Market Overview for AI Context Platforms, By Kjell Carlsson, Afraz Jaffri, etc., 13 August 2026.
*GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
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. Learn more at neo4j.com.
©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.
View source version on businesswire.com: https://www.businesswire.com/news/home/20261020474163/en/
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