# Data dbt Exposures and Downstream Dependencies
Status: public
Confidence: medium (0.685) (verified)
Last verified: 2026-06-03
Generation: ai_structured


## TL;DR

dbt exposures help agents trace which dashboards, applications, notebooks, and ML workflows depend on modeled data.

## Core Explanation

Data agents should not treat a warehouse model as isolated. A column rename, freshness failure, or contract change can affect dashboards, applications, data science pipelines, and semantic metrics downstream.

dbt exposures encode those consumer-facing dependencies in the project graph. Agents should inspect exposure names, owners, maturity, URLs, depends_on entries, generated docs, manifest exposures, parent maps, and child maps before recommending a model change or backfill.

## Source-Mapped Facts

- dbt documentation says exposures make it possible to define and describe a downstream use of a dbt project, such as a dashboard, application, or data science pipeline. ([source](https://docs.getdbt.com/docs/build/exposures))
- dbt exposure documentation says the depends_on property lists refable nodes including metric, ref, and source. ([source](https://docs.getdbt.com/docs/build/exposures))
- dbt manifest documentation lists exposures, parent_map, and child_map as top-level keys in manifest.json. ([source](https://docs.getdbt.com/reference/artifacts/manifest-json))

## Further Reading

- [dbt Exposures](https://docs.getdbt.com/docs/build/exposures)
- [dbt Manifest JSON File](https://docs.getdbt.com/reference/artifacts/manifest-json)