A simple mental checklist for data teams putting AI agents next to their warehouse, and where to go deeper.
Someone in a Monday review asks the question you knew was coming.
"Should we plug an AI agent into the warehouse?"
The room turns to the data team. The interesting part is not which model to pick. The interesting part is everything around the model.
A good model is easy to swap. What is hard to swap is what the model can see, what it is allowed to do, what a single answer costs, and how locked in you become the day you say yes.
Four questions cover most of that. I think of them as Context, Control, Cost, Choice.
They are simple enough to keep in your head during a design review. That is the whole point.
An agent is only as good as the data and definitions it can reach.
If your orders table has three columns that look like revenue and nobody agrees which one is right, the model will pick the wrong one confidently. If your business glossary lives in a slide deck, the model does not have a glossary.
On Databricks this is where Unity Catalog, table comments, tags, and Genie spaces do the quiet work. A well-described table is context. A dashboard nobody documented is not.
Before you add an agent, ask: could a smart new hire answer this question from what is written down today? If the answer is no, the agent will not either.
Related reading: Context Is the New Pipeline.
Reading a table is one decision. Writing to one is a different one. Calling an external API on behalf of a user is a third.
Unity Catalog permissions, row and column filters, service principals, and audit logs are the boring parts of an AI project. They are also the parts that decide whether you sleep well.
A good rule of thumb: an agent should never have permissions a junior analyst would not be trusted with on day one. Start narrow. Widen only when you can see who did what.
Every answer has a token cost, a compute cost, and a human review cost. The last one is the easiest to forget.
A chatbot that gives a wrong number costs you the meeting where someone acts on it. A retrieval step that scans a huge table on every prompt costs you a warehouse bill nobody predicted.
Two habits help. Measure cost per useful answer, not per call. And keep a small "was this right?" feedback loop in the product from day one, even if it is just a thumbs up.
The agentic space is moving fast. The interesting question is not "which vendor should we bet on this quarter?" It is "what happens the day we change our mind?"
Keep your data in open formats. Delta and Iceberg are open. Your notebooks and pipelines should be portable. Your prompts and evals should live in your repo, not inside a vendor console.
Pick tools you can leave. Then you can commit fully without being trapped.
BricksNotes teaches you the Databricks craft: how Delta behaves, how Unity Catalog governs, how a Lakeflow pipeline actually runs. That is the ground under your feet.
The four questions above are older and bigger than any one platform. They belong to a wider conversation about how data teams work in the agentic AI era.
That is exactly what our sister project, The Context Advantage, is about. It is a living book, written for data professionals, that goes deep on Context, Control, Cost, and Choice as a way of thinking, not a set of tools.
If today's article gave you a checklist, that book gives you the reasoning behind it.
You do not need to buy anything to use the four questions. Take them into your next design review and see what happens.
If they help, the first three chapters of The Context Advantage are free. Read them the way you would read a good essay: slowly, with your own project in the back of your mind.
Then come back to BricksNotes and build the thing.
Related reading: Context engineering is becoming a real job skill for data engineers.