Data agents fail when there is a problem with data foundation underneath them. Inconsistent schemas, sampled events, broken cross-domain journeys. When the foundation is shaky, even the best AI returns answers you can't trust. This blueprint shows you how to fix that.

You'll build a semantic layer on top of Snowplow's behavioral data in Snowflake, connect it to a Snowflake Intelligence agent, and give your team accurate, natural language answers from their data. No SQL required. No guessing from the model. Snowplow's schema-validated, enriched behavioral data gives the agent something solid to reason over, and with the right semantic layer in place, accuracy exceeds 90%.

The blueprint provides a deep dive on the following key topics:

• Why Snowplow's schema-validated data is uniquely suited to agentic analytics (and where other tools fall short)
• How the semantic layer works, what Snowplow pre-builds for you, and what your team needs to add
• Step-by-step setup: semantic view creation, YAML template, and agent configuration in Snowflake Intelligence
• A ready-to-use prompt library organized by use case: traffic analysis, funnel drop-off, anomaly detection, and more
• 5 example queries that showcase what becomes possible with Snowplow-quality data underneath