dbt_context_engineering: modeling the context your AI agents read
Every company is trying to put AI to work on its own data right now, and most are discovering the same thing: the model isn't the hard part. An agent's answer is only ever as good as the context behind it, and in most organizations that context was never modeled for a machine to read.
As a community, we've traditionally been asked to build the analytical systems our teams need to understand measurable performance, and that work already serves AI: an agent can pull a governed, tested metric through the semantic layer today. But the reasons behind the numbers sit in call transcripts, support threads, and contracts that analytics tooling has never been able to reach. Shaping that data so AI can retrieve it, trust it, and cite it has a name: context engineering. It's a practice we believe the dbt community is unusually well positioned to lead.
That belief comes from the observation that context engineering isn't a new discipline you have to learn from scratch. It's analytics engineering, pointed at a new consumer. The habits that made your metrics trustworthy (version control, testing, documentation, lineage) are exactly what AI context is missing today. So we're releasing dbt_context_engineering, an open source package that brings the first set of context engineering patterns into the framework you already use. This post walks through what's in the package, what it unlocks, and why we want to work out the rest of the practice with the community.