Neo4j makes the case for knowledge graphs as shared context for AI agents
Knowledge graphs can give enterprise agents a shared understanding of how data, business rules and processes fit together. However, that context becomes harder to maintain when each new agent carries its own version of what the business knows. Organizations have become more proficient at building agents, but their results still vary widely. The difference often […] The post Neo4j makes the case…
Knowledge graphs enable enterprise agents to have a unified understanding of data, business rules, and processes. However, as more agents are created, each may maintain its own version of business knowledge. Jesús Barrasa, field chief technology officer at Neo4j Inc., emphasized the importance of presenting enterprise knowledge to agents along with their data.
This shared knowledge layer allows agents to utilize context, explain their responses, and reuse knowledge across tasks. As organizations adopt agents, they encounter similar challenges faced by earlier reporting systems that yielded inconsistent results. A knowledge layer is a structured representation of an organization's data assets, policies, and processes.
It enables agents to understand how they arrived at an answer and offers explainability. Building this layer incrementally, starting with one use case, facilitates aligning subsequent applications. Investments in knowledge layer construction should consider the overall return, as well as the negative impact of diverging results across agents.
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