The Agent Context Layer for Trustworthy Data Agents

Enterprise AI agents fail not because models are weak, but because they lack the right context. The solution is an 'Agent Context Layer' comprising five components: an analytic semantic model (metrics, dimensions mapped to data), a relationship and identity layer (ontology for cross-domain entity resolution), operational playbooks (procedural routing and policy enforcement), provenance and explainability records, and event/decision memory. Internal experiments show that augmenting agents with a plain-text data ontology improved answer accuracy by 20%, reduced tool calls by 39%, and cut latency by 20%. Unlike prompt engineering, this approach produces versioned, auditable, governable artifacts. AI agents like Snowflake Cortex Code can help build and maintain these context layers by reading existing docs, query history, and ontologies, with humans kept in the approval loop.