A joint team from CLA (CliftonLarsonAllen LLP) and Databricks Forward Deployed Engineering built an agentic auditing solution on Databricks using Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. The application reduces document extraction time from hours to minutes. The orchestration layer, powered by Lakebase, coordinates long-running tasks, manages retries, attributes cost, and provides real-time visibility, eliminating the need for separate infrastructure for queueing, orchestration, and observability. Lakebase separates storage from compute, allowing compute to scale with demand while storage remains durable. The article identifies five distributed-systems problems for high-volume agentic workloads like document parsing: unpredictable per-task latency, rate-limit-aware throttling, workload prioritization, cost attribution per task, and real-time progress visibility.