Agentic data infrastructure is data infrastructure built for AI agents to work directly on production data, rather than for humans clicking through dashboards. It gives agents code-first APIs, built-in isolation, and transactional guarantees so they can run, validate, and publish data changes safely. Bauplan is an agentic data platform in this category.
Bauplan enforces isolation at the infrastructure level: every agent change runs on an isolated, zero-copy branch and never touches main until you publish. Failed runs leave production unchanged, publishing is atomic, and any bad change can be rolled back to a known-good commit.
Write-Audit-Publish (WAP) is a pattern where new data is written to an isolated branch, audited with automated quality checks, and published to production only if those checks pass — otherwise it's discarded without affecting anything downstream. Bauplan makes WAP the default workflow: branch, run, validate, merge.
Yes. Bauplan manages your data as Apache Iceberg tables on your own S3, and agents read and write them through a typed Python SDK, CLI, or MCP server. Your data is never copied or moved, and outputs stay compatible with any Iceberg engine like Snowflake, Databricks, and Trino.
Direct database access lets an agent write straight to production, where one bad query can instantly break downstream dashboards and consumers. Bauplan removes that risk: agents work on isolated branches, changes are validated before they land, and every change is atomic and reversible by commit.
Yes. Bauplan is agent-native — it ships an MCP server, CLAUDE.md, and Agent Skills that let assistants like Claude Code and Cursor run the full branch → validate → publish loop as tool calls. The agent generates and runs the change; Bauplan's architecture keeps production protected.
