aiconfig-context-advanced

Cardinality-safe contexts prevent explosion by using low-cardinality keys for user and organization attributes while storing high-cardinality data as private attributes. This approach scales reliably across enterprise, SaaS, and insurance workloads without destabilizing targeting rules.

Compose user, organization, workspace, and agent-graph contexts into a single multi-context builder. This pattern enables stable targeting across orchestration systems and MCP toolchains while maintaining session and runtime attributes for complex enterprise scenarios.

Yes. Multi-context patterns with private attributes and low-cardinality keys support high-trust, regulatory-compliant targeting. Templates include structures for orchestrator integration and agent-graph attributes needed in insurance and compliance-heavy workflows.

Mark high-cardinality fields as private attributes and bucket numeric values into ranges. This isolates cardinality risk from targeting logic while preserving the data for segmentation and analysis in enterprise customer onboarding and B2B workflows.

Low-cardinality keys define stable targeting dimensions with few unique values. Bucketed numeric fields group continuous data—like user age or session count—into discrete ranges to reduce cardinality while keeping analytical fidelity.

Not always, but including them enables full integration with MCP toolchains and orchestration systems. Session and orchestrator attributes unlock runtime-aware targeting and agent-graph coordination for enterprise deployments requiring high-trust context handling.