Abstract

Customer-facing AI systems increasingly span heterogeneous components such as event ingestion, streaming and batch feature computation, feature stores, model training, online inference, experimentation, and downstream decision services. Failures across these layers can silently degrade customer experience through feature staleness, training-serving skew, or distribution drift. Traditional microservice observability often stops at service telemetry and does not preserve the semantic lineage required to explain why a prediction occurred. This paper proposes an end-to-end observability approach for Customer AI that unifies distributed tracing, data lineage, feature provenance, and prediction monitoring into a single correlation fabric. We present a reference architecture that couples runtime telemetry with lineage events, binds features and predictions to immutable identifiers, and enables cross-system diagnosis workflows such as identifying which upstream data changes impacted a cohort of predictions. The resulting blueprint supports privacy-aware attribution, governance expectations, and operational actionability for production AI.