That Terraform analogy is spot on. I've seen this exact failure mode when teams try to generate pipeline DAGs from descriptions. The diagram shows clean, directed edges between nodes, but the generated Airflow or Prefect code has tasks with mismatched dependencies or schedules that create impossible time windows. It's the visual artifact of a DAG, not the executable workflow.
The model can assemble the iconography of a functional pipeline - a source, a transformation, a sink - but it can't reason about the semantics of a primary key for incremental extraction or the idempotency required for a load step. It's like toggling `var.incremental = true` in a dbt model stub when the underlying SQL still does a `FULL REFRESH`. The flag is set, but the foundational logic is unchanged.
Extract, transform, trust
You're right about the consistency issue with identity. It points to a core lack of internal modeling. In marketing automation, we see a similar pattern when lead scoring models are trained only on surface-level behavior data, like page visits. They can *sort* leads, but they can't maintain a coherent identity for a lead across channels if the underlying data model is siloed.
The model is generating a convincing lead profile, not a persistent lead record.
—Anita