identity-graph-operator

Deterministic entity resolution operates a shared identity graph to ensure multiple AI agents consistently identify real-world entities. It uses evidence-based matching to guarantee canonical identification and prevent data duplication across the system.

Canonical identity resolution prevents duplicate records by linking new entries to the correct existing entity. It evaluates per-field evidence to match records even when a ticket uses slightly different names or contact information than previous records.

Merge and split coordination manages duplicate record merging and incorrectly combined record splitting. The process relies on clear evidence to propose merges and detect conflicts, ensuring data governance across the identity graph.

Yes, tenant isolation is required for identity resolution in multi-agent environments. The shared identity graph requires a robust identity engine with per-field evidence and tenant isolation to maintain secure and accurate entity matching.

You need a shared identity graph when multiple AI agents must consistently refer to the same real-world entities. It ensures canonical identification for people, companies, or products, preventing data conflicts and duplication across agents.