Overview

Bauplan is driven entirely by code, which makes it a natural fit for AI agents. The whole lakehouse is programmable through a CLI and a Python SDK: there is no UI step an agent has to click through.

Git-for-data branching is what makes it safe to hand that control to an agent. The agent works on an isolated data branch, validates its changes with expectations and the Write-Audit-Publish pattern, and merges into the main branch only when the checks pass. Nothing reaches production data until it has been reviewed, and every change is a commit you can inspect or revert.

There are three complementary ways to use agents with Bauplan. They are not mutually exclusive: an assistant can use the MCP server for live access, run Skills for structured workflows, and read the documentation as context, all in the same session.

Agent Skills

Reusable recipes for the main workflows, including pipelines, ingestion, exploration, and debugging.

MCP server

Give an assistant live access to your lakehouse through tool calls.

Context for agents

Feed AGENTS.md, llms.txt, and the Markdown docs to any LLM or IDE assistant.

Learn more

The Bauplan blog covers running AI and agents safely on your data, alongside technical deep-dives on the platform. The team also publishes peer-reviewed research on the data systems behind Bauplan and their application to AI and agentic workflows. Find our latest results on Google Scholar.