Lesson 1 of 10 · Foundations
What agent-ready data actually means
Short answer
Agent-ready data is data an AI agent can find, understand, trust and use within access rules, without a person writing the context by hand for every question.
By The Lineage Desk · Best Data Catalogs · Published
Desk research from public vendor pages. Editorial assessment.
3 min read
Most data teams have already met the first version of the problem. Someone connects a large language model to the warehouse, asks a business question, and gets a query that runs without error and returns the wrong number. The SQL was valid. The context was missing.
What does an agent need to know?
Think of the questions a careful analyst asks before touching a table. Which table holds the answer? What does this column mean in this company, not in general? Is this the certified version of revenue or a scratch copy someone built last quarter? Does this field depend on personal data that should not leave the team? Am I allowed to see these rows at all?
An agent needs the same answers, and it needs them at query time, in a form it can read. That is the practical definition of agent-ready data: the assets an agent may touch come with their meaning, their provenance, their trust status and their access rules attached.
Why is a model not enough on its own?
A model can guess the meaning of a column called net_rev_adj from its name. It cannot know that finance changed the definition in March, that the dashboard everyone trusts filters out test accounts in a Tableau calculated field, or that the table it found is fed by a deprecated pipeline. That knowledge lives in metadata scattered across the warehouse, the transformation layer and the BI tools.
What are the four layers of readiness?
We use four layers on this site. Discovery: the agent can find the right asset among thousands. Meaning: business definitions and metric logic are available, including logic that lives in BI tools rather than in a semantic layer. Trust: certification, lineage and quality signals tell the agent what is safe to use. Control: access rules and sensitivity labels travel with the context, scoped to the person or task the agent is serving.
The ranking on this site turns these layers into criteria. Agent access covers how context reaches the agent. Lineage, automation and governance cover discovery, trust and control. Business definitions cover meaning. Answer checks cover whether you can tell when the agent is still wrong.
Where does the data platform team come in?
Every one of these layers is built from systems the data platform team already runs. That is why readiness projects tend to land with the head of data or the platform lead rather than with an AI team. The work is not prompt writing. It is making the stack explain itself.
The next lesson separates the three kinds of product that claim to do this: catalogs, semantic layers and AI context layers.
Key points
- Valid SQL is not a correct answer: agents fail on missing context, not syntax.
- Readiness has four layers: discovery, meaning, trust and control.
- The context comes from systems the data platform team already runs.