Data lineage explains information history. Execution Lineage explains application behavior history.
Connect source data, retrieved context, calculated facts, identity, authority, policy, decisions, human approvals, durable state, capability attempts, and external effects under the same Execution Instance.
Preserve evidence in the Data Plane
The AI Data Plane preserves or references evidence artifacts and typed lineage relationships that survive execution.
Explore Behavior IntelligenceInvestigate behavior through Behavior Intelligence
Behavior Intelligence uses those records to inspect one execution, compare many, reconstruct historical behavior, and perform governed replay.
Keep provenance, lineage, causality, and evidence distinct
Use the Published Graph and Execution Instance as evidence anchors
Evidence becomes easier to join when it attaches to the application version that was permitted to run and the realized execution that actually ran.
Preserve what crossed the decision boundary
Origin, eligibility, selection, exclusion, transformation, freshness, order, truncation, and final use remain connected to the downstream Decision Object.
Explore Context AssemblyPreserve the chain of judgments
Deterministic validation, model proposals, sufficiency gates, policy obligations, routing, human approval, revalidation, and final authorization stay connected.
Carry authority, state, versions, and effects into the evidence record
Authority context
Subject, actor, workload, delegation chain, tenant, legal entity, purpose, assurance, policy version, and obligations.
Durable transitions
Published Graph version, proposal revision, wait state, arriving event, revalidation result, and resource revision.
Operation to effect
Invocation proposal, Business Operation Identity, attempts, timeouts, retries, reconciliation, compensation, and Effect Receipt.
Preserve typed relationships, not only chronology
A timeline shows sequence. An evidence graph shows how one artifact formed, constrained, authorized, attempted, or confirmed another.
Map the evidence spine for one consequential AI outcome.
Use an architecture review to define source-to-effect lineage, evidence coverage, causal bindings, permission-aware inspection, and integrity requirements.