Self-Healing Semantic Layers with Snowflake Cortex AI

Many organizations are adopting AI agents to accelerate analytics and application development, but few have a reliable way to ensure those agents continue producing accurate results over time. As data changes, business logic evolves, and semantic models drift, AI-generated answers can quietly become unreliable without teams realizing it.

Watch this on-demand session to see how Sigma built J.A.K.E. (Just Another Knowledge Engine), a self-healing AI agent powered by Snowflake Cortex that monitors semantic models, detects drift, and automatically generates fixes before issues impact users.

In this session, you'll learn how to:

• Detect semantic drift before it affects dashboards and AI-generated answers
• Build feedback loops that continuously improve AI outputs
• Use Snowflake Cortex to monitor, diagnose, and repair semantic models
• Automate semantic view generation and maintenance with AI agents
• Create governed AI workflows that improve accuracy over time
• Reduce the manual effort required to maintain trusted analytics

This technical walkthrough is designed for data platform teams, analytics engineers, AI engineers, BI leaders, and anyone responsible for building or governing AI-powered analytics on Snowflake.

Watch the on-demand recording to learn how Sigma built J.A.K.E. and see a practical approach to creating self-healing, governed AI workflows on Snowflake.