A plain search or lookup finds one matching record. A context graph represents the relationships between records: this customer placed these orders, this order contains these line items, this vendor is tied to these contracts, so a query can follow those connections rather than treating each record as isolated.

For an AI agent, this matters when a request requires connecting facts across systems: "has this vendor's contract expired, and does that affect the open purchase order" requires traversing a relationship between a contract record and a PO record, not just retrieving either one in isolation.

Frequently Asked Questions

How is a context graph different from a regular database?

A database stores the records. A context graph specifically represents and makes traversable the relationships between those records, so a system can answer "what's connected to this" rather than only "what matches this query."

Why do AI agents need this instead of just searching each system directly?

Real business questions often span systems, a support ticket references an order, which references a shipment, which references a carrier. Without a way to follow those relationships, an agent can only answer questions that live entirely inside one system at a time.

Is a context graph the same thing as a Company Brain?

Related but not identical. A context graph is a structural way of connecting records; a Company Brain is the broader idea of shared organizational memory, of which a context graph can be one underlying mechanism.

Does building a context graph require restructuring existing systems?

Not necessarily. It can be layered on top of existing systems as a mapping of how their records relate, without requiring the underlying systems themselves to be rebuilt or merged.