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News3 min read·2026-10-21DisconfirmAI Editorial

AI Civic Data Company Hamlet: Production Data Branching Lets Coding Agents Independently Verify Each PR

Hamlet Lead Developer Erich Douglass and Senior AI Engineer Jeff Hui describe a concrete change: after giving each pull request its own branch of production data, AI coding agents can run data pipelines to completion and check their own results, while a human still approves the merge. Previously, preparing test data consumed several hours per engineer each week.

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Original publication time 2026-10-20 22:45 GMT+8

Hamlet is an AI-powered civic data platform that indexes and transcribes city council, planning commission, and zoning board meetings across the United States, allowing real estate developers, journalists, researchers, and citizens to search meeting content and track local policy developments. CEO Sunil Rajaraman says customers want to know whether a council member will approve their housing project, why or why not, and what history the city has that might make it hesitate. This business requires processing extremely messy data from more than 7,200 governing bodies, and the engineering team is small—velocity matters more than almost anything.

Lead Developer Erich Douglass recalled the previous state: they ran one production instance and one staging instance on AWS RDS, with much of their work involving tuning data pipelines against very specific data. Every pull request required 15 to 30 minutes to get staging into the right state before they could run validations. Senior AI Engineer Jeff Hui added that 15 to 30 minutes was the best case—when you happened to copy exactly the right data. Each engineer could spend several hours a week keeping test data current, including cloning pieces of production locally to chase down issues.

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After switching to Databricks Lakebase Postgres, every pull request automatically gets its own branch of production data, connected to its preview environment. Douglass says when the preview deploys, the data is ready—they can test against production data and even write to it without touching production. Hui says now when they hit a bug, they have the exact data that caused it, and they can check the fix against that same data.

But what truly changed the workflow was not the saved minutes. Hui says branching makes their workflow a much better fit for AI coding agents. Each pull request is isolated from production; they give an agent like Claude or Codex access to its branch and let it run its own checks. The agent runs the data pipelines to completion and looks at whether the change does what it should. Every pull request gets that first pass from an agent. By the time a person looks at it, they are already fairly confident it works. A human still approves the merge, but more and more of the rest is automated.

The prerequisites of this workflow deserve attention: the agent needs an isolated, complete environment containing real data to run end-to-end pipelines and judge results on its own. If the data is synthetic or stale, the agent's verification does not equal verification against real scenarios. Hui stated a similar principle when discussing video speaker identification—

You can't hand a model a video and hope for the best. You have to collect everything around it that helps it make the right call.

For coding agents, the surrounding information is that real data.

It should be noted that this is a customer interview published on the official Databricks blog. Douglass and Hui describe their own team's actual practices; the time savings are self-reported figures without a stated measurement method. Hamlet is a small team, and whether their workflow applies to larger teams or different technology stacks is not discussed in the original.

Sources

  • 原始出处databricks.com↗
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