The Agent Context Layer for Trustworthy Data Agents

The Agent Context Layer concept signals a maturation we're overdue for in data platforms. I've seen too many GenAI-on-data pilots fail because they lacked semantic grounding—models hallucinate joins, misinterpret business logic, and executives lose trust fast. What Snowflake's framing captures is that trustworthy agents need three things working in concert: explicit semantic models (think dbt contracts or data catalogs with real teeth), domain ontologies that encode business rules, and operational playbooks that define when agents should escalate or refuse requests. Architecturally, this means data teams can't just expose tables anymore. We need to build semantic layers—whether through tools like dbt, cube.dev, or native platform features—that agents can reason about safely. For teams running on Snowflake, this might mean investing in governance frameworks and semantic models now, before pushing agents into production. The broader implication is clear: the next competitive advantage isn't raw data access, it's trustworthy interpretation. Start documenting your domain logic explicitly today, or your agents will become expensive hallucination machines tomorrow.