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Databricks simplifies AI agent orchestration with Lakebase Postgres

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feature announcement

CliftonLarsonAllen (CLA) collaborated with Databricks to build a fully Databricks-native solution for agentic auditing and document processing. The solution leverages Lakebase Postgres as an orchestration backbone, eliminating the need for external infrastructure like message brokers or schedulers. This approach addresses challenges such as unpredictable task latency, rate-limiting, workload prioritization, cost attribution, and real-time visibility for long-running agentic tasks.

  • →Lakebase Postgres as a durable task queue for agentic workloads
  • →Databricks-native architecture for agentic document parsing
  • →Real-time operator dashboard with low-latency observability
  • →Databricks-native agentic auditing solution reduces document processing time
  • →Concurrency-safe task dequeuing with Postgres FOR UPDATE SKIP LOCKED
Features (3) ›
  • Lakebase Postgres as a durable task queue for agentic workloads

    The solution employs Lakebase Postgres tables as a durable, concurrent, and crash-resilient queue for long-running agent tasks, eliminating the need for brokers, caches, or schedulers. It separates storage from compute, allowing for independent scaling.

  • Databricks-native architecture for agentic document parsing

    A reference design integrates Lakebase, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes into an end-to-end pipeline for agentic document parsing without external infrastructure.

  • Real-time operator dashboard with low-latency observability

    Postgres LISTEN/NOTIFY triggers paired with Server-Sent Events (SSE) are used to build a low-latency operator dashboard that tracks costs and tasks with zero overhead.

Enhancements (2) ›
  • Concurrency-safe task dequeuing with Postgres FOR UPDATE SKIP LOCKED

    The task queue implementation uses Postgres's FOR UPDATE SKIP LOCKED clause to ensure that multiple workers can safely dequeue tasks concurrently without selecting the same task.

  • Prioritized task processing and FIFO ordering

    Tasks are processed based on priority, with higher-priority tasks dispatched first, while maintaining FIFO (First-In, First-Out) ordering within each priority level using an ORDER BY clause.

Notes (1) ›
  • Databricks-native agentic auditing solution reduces document processing time

    CliftonLarsonAllen (CLA) developed a document processing application on Databricks that significantly reduces extraction time from hours to minutes. The application utilizes Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes.

Read the original announcement →

https://www.databricks.com/blog/simplify-ai-agent-orchestration-lakebase-postgres

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