Tired of choosing between shipping quickly and shipping reliable outputs? 

Whether you're building ML models, AI agents or AI-powered transformations, the reality is: broken lineage = broken trust. When your AI agent hallucinates, your model drifts or your LLM-generated transformation produces weird results, production deployments are stopped.

When you can trust and explain your AI outputs, it means less time debugging, better adoption from users and scalable business value.

Join Snowflake and dbt Labs to learn how top teams ship AI faster by building trust in.

During that session, we’ll show you a live dbt + Snowflake implementation that turns responsible AI into an accelerator so you can deploy models, agents and AI transformations with confidence.

What you'll learn more about:
 

• Instant lineage visibility: Track every step from raw data through AI transformations to model/agent outputs using dbt

• Automated context: Use Snowflake's object tags and OSI integration to prove exactly what data and prompts influenced which AI outputs

• Explainability that scales: Leverage the semantic layer to answer "Why did the agent do that?" or "What data trained this model?" without adding to technical debt