How Data 360 data graphs deliver trusted customer context to Agentforce
The engineering team built Data 360 data graphs to solve the context gap for Agentforce by unifying fragmented customer data across accounts, entitlements, and products. Instead of running multiple queries at runtime, the system pre-aggregates relationships into cohesive data products that agents can call directly.
Resolving customer identity required a partitioned architecture that keeps the broader identity graph separate from customer success views. This ensures that prospect data and information belonging to different tenants remain strictly isolated while still supporting complex many-to-many relationships.
Performance was optimized by analyzing access patterns, designing smaller multi-graphs, and building targeted indices to avoid full table scans. The team shifted from static data models to flexible structures that support semantic search and unpredictable agent queries, ultimately achieving median response times under two hundred milliseconds without dedicated autoscaling infrastructure.