‍How autonomous data products make agentic interaction with enterprise data reliable, safe, and reproducible

Data agents are quickly becoming the new interface to enterprise data. They don’t just answer questions. They find sources, interpret business meaning, generate queries, compare results, and trigger workflows. That raises the bar for data architecture.

A human analyst working from a dashboard can notice ambiguity, ask a colleague, or sense that a number looks off. An agent can’t. If the meaning of a metric, the freshness of a source, the access policy, and the ownership model are scattered across disconnected systems, the agent is left guessing. That’s how you end up with wrong answers, unsafe data exposure, and results nobody can reproduce.

Our new white paper argues that these are data architecture problems, not model problems. A better model writes better SQL. It can’t fix missing semantics, weak contracts, stale data, or governance that exists only in documentation.

Platforms like Databricks Genie are pointing in the right direction by building context, governance, and verification into the agent experience. But that works only inside one platform, and most enterprises don’t live inside one platform. They run Snowflake and Databricks, plus operational systems, SaaS apps, document stores, and policy engines, all spread across domains and regions.

What you’ll learn

• Why data agents aren’t SQL copilots. Before writing a query, an agent has to decide which data to use, whether it’s allowed to use it, and whether the result can be trusted.
• The shift from schema-first to semantic-first. Meaning, policy, and quality have to be built into data products as they’re produced, not annotated by hand afterward.
• The nine requirements of reliable data agents. These include discovery by business intent, glossary data products, contracts, executable policies, safe promotion, and governed MCP endpoints.
• A reference architecture for production data agents across a distributed enterprise estate.
• A worked example that follows a churn-risk question from user prompt to governed, auditable answer.
• An operating model where central teams own the standards and domain teams own the meaning.

If you’re building data agents and your enterprise isn’t all-in on a single lakehouse, this paper lays out the missing layer.

Download the paper here.