Graph database company Neo4j on Thursday unveiled two products targeting a common enterprise challenge: building artificial intelligence (AI) agents that can access and reason over organizational data.
Neo4j Aura Agent lets users build and deploy AI agents connected to enterprise data within minutes. The platform provides automated orchestration for graph-based knowledge retrieval.
The company also announced a Model Context Protocol (MCP) Server that integrates graph-based memory into existing AI applications. It supports natural language queries and automated management of Neo4j database instances.
Conor O’Shea, AI architect at Daimler Truck, said of the approach, “Enterprise knowledge graphs represent critical infrastructure for reliable agentic AI. At Daimler Truck North America, we’ve seen how Neo4j’s graph capabilities bring the accuracy and contextual reasoning that AI systems need.”
Healthcare and pharmaceutical company QIAGEN said it anticipates advances in drug discovery applications. “Neo4j Aura Agent promises to improve healthcare by designing and deploying AI agents that create comprehensive knowledge graphs from our trusted biomedical knowledge,” said Nitin Sood, the company’s senior vice president of product innovation.
Additionally, Neo4j said it is investing $100 million to position itself as essential infrastructure for AI systems. The investment will fund new products designed to help enterprises build AI agents and support AI startups.
Neo4j’s new Startup Program will enroll more than 1,000 AI-native companies in the next 12 months, offering cloud credits, technical support, and go-to-market assistance. The program currently has 208 members, including Firework, Mem0, and Zep.
Rivio, which builds AI agents for procurement, is among the early participants. “Neo4j enables us to model that complexity with the accuracy our customers require,” said CEO Hala Jalwan.
The funding comes as companies struggle to deploy generative AI at scale. MIT research found 95% of GenAI pilots fail to deliver returns, citing poor model quality without proper context and insufficient memory as key failure points.
“Agentic systems are the future of software,” Neo4j CEO Emil Eifrem said in a statement. “They need contextual reasoning, persistent memory, and accurate, traceable outputs, all of which graph technology is uniquely designed to deliver.”
