Building an Agentic Observability Layer for Data Pipelines
Summary
This article explains how an agentic observability layer can help diagnose failed data pipelines by collecting logs, metadata, data quality results, and table state into a structured incident context. It argues that the layer should classify failures, explain likely root causes, and recommend next steps without taking autonomous action in production. The piece emphasizes that the approach works best when existing pipeline telemetry is already well structured and complete. It also lays out a clear safety boundary: the agent can triage and recommend, but engineers and approval workflows must handle retries, schema changes, permissions, and other risky actions. The article closes by recommending structured outputs and careful evaluation using synthetic and historical incidents before production rollout.