Beyond ETL and Reverse ETL: Designing Reliable Data Systems for Agentic Observability

Data teams have focused on moving data efficiently: using ETL to bring it into the warehouse and reverse ETL to activate it across their business systems. In the age of AI, data doesn’t just inform decisions, it drives them. Data no longer just moves into the warehouse, it moves in, out, and across the entire data stack.
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Pipelines can run successfully but issues like stale data, missing records, or schema changes can still break your pipelines, ultimately powering incorrect outputs applications and AI driven workflows. The result is a system that appears reliable on the surface, can quietly produce bad data and broken pipelines at scale.
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In this session, we’ll walk through a real-world scenario where a pipeline runs end-to-end without failure, yet introduces a hidden data issue that flows into an AI-powered use case and leads to incorrect automated decisions. From there, we’ll explore why today’s data stacks lack the visibility and control needed to catch these issues, and how the modern data stack is now moving towards a unified system with built-in observability, enabling reliable data flow across pipelines, applications, and AI-driven workflows.