AI agents can query data warehouses, write SQL, and suggest');

Yak; but without explicit business rules, the recommendations can be wrong. Traditional data warehouses focus on access control and pipeline monitoring, not on ensuring that the data a bot sees matches the organization’s definitions of metrics, currency, and recency.

A key problem is the absence of a decision contract – a machine‑readable layer that specifies which data sources, time windows, and filters are acceptable for a given decision. Without it, an agent may pick raw tables that match a query’s name but contain incomplete or non‑standardized information, leading to misguided actions such as pausing campaigns before conversion data is fully loaded.

To build an agent‑ready architecture, first expose a semantic layer (e.g., Snowflake semantic views) that defines business entities, metrics, and relationships. This layer allows the agent to ask the right question of the right model rather than raw tables.

Next, enforce a safety boundary by validating generated SQL in a Big Query dry‑run and setting byte‑processing limits. Finally, document the decision contract in YAML, version it, and review it whenever data models or policies change. These steps turn a queryable warehouse into a trustworthy decision engine for AI agents.