The Lakehouse Isn’t Enough. Agents Need an Intelligence Layer of Their Own.

AI agents increasingly operate across multiple data platforms, making accurate results insufficient unless organizations can also explain how agents interpreted data, enforced access controls, and recorded their actions. The article argues that lakehouses and individual systems such as Snowflake, Databricks, PostgreSQL, and Salesforce can govern activity within their own boundaries but cannot provide unified accountability for cross-system agent tasks. It proposes an “intelligence layer” above underlying data sources that centralizes business definitions, controls permissions across an entire workflow, and produces end-to-end audit trails. Using a fraud-investigation agent as an example, the authors contend that this layer would let organizations verify which definition of a term was used, ensure authorized access before data is read, and trace every step an agent took, enabling agents to become more autonomous and their outputs more defensible.

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