agency-identity-graph-operator

Deterministic identity resolution prevents data duplication by mapping disparate records to a single canonical entity ID. It applies normalization, blocking, and field-level scoring logic to ensure multiple agents consistently resolve the same real-world entity.

Deterministic identity resolution in a multi-agent system is the process of resolving disparate records into canonical entities. It maintains graph integrity by applying field-level scoring, audit trails, and evidence-based merge proposals to prevent cascading errors and conflicting agent actions.

You resolve duplicate records like Bill Smith and William Smith by applying fuzzy matching and normalization logic. This maps disparate name variations to a single canonical entity ID within the graph database, preventing conflicting agent actions and data fragmentation.

Yes, you can review merge proposals before mutating the identity graph. The system generates evidence-based proposals with per-field confidence scores, allowing for human or agent-based review to verify entity matching accuracy before any changes are applied.

You manage conflict between agent actions through dedicated conflict management logic. The system detects and flags disagreements, such as simultaneous merge and split proposals, to maintain graph integrity and prevent cascading errors across the multi-agent environment.

Performing entity matching across diverse data sources requires a system that maintains graph integrity through deterministic matching and audit trails. A graph database structure supports the canonical resolution and evidence-based merge proposals needed to prevent cascading errors.