Why Enterprise AI Needs Less Guesswork and More Structure
Turn business meaning into governed execution—so the model has less to reconstruct at runtime.
An analytics agent can write valid SQL and still answer the wrong business question.
The database executes it correctly. The mistake is in what “revenue,” “enterprise customer,” or “last quarter” was taken to mean.
The failure becomes visible in the agent, but it may have started in the business definitions and data model underneath it.
Let the Workflow Define the Agent
Suppose the capability is to let employees ask for approved financial metrics in natural language.
The workflow still contains different kinds of decisions: understand the request, identify the metric, apply its calculation, query the data, and explain the result.
Some require interpretation. Others already have an agreed answer.
If finance has defined the metric and the data team has established its relationships, why ask the agent to reconstruct them?
Agent autonomy should follow that division...
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