Another step in the development of my AI SQL Agent: I’ve added a centralized Business Rules layer that allows me to control how the AI Agent works with the database without hardcoding every instruction into the application logic.
Instead of relying only on a large system prompt, I created a dedicated database table where architectural and business rules can be stored, maintained, and provided to the AI Agent as part of its working context.
For example, an architecture rule can define standards for creating dimension tables:
➡️ When a user asks the Agent to create a dimension table, it must include:
Code — default length 3
Name — default length 20
So instead of simply generating whatever table structure the LLM considers appropriate, the Agent follows the architectural standards defined for the environment.
Another example is a business rule for inventory movement.
If a user asks:
“Move 10 units of Product A from Warehouse 1 to Warehouse 2.”
the Agent understands that this operation should create two records in the moving_product table:
Warehouse 1 → -10
Warehouse 2 → +10
The important idea here is that the AI Agent does not need to “know” these organization-specific rules during model training.
The rules become part of the data and configuration layer.
User Request → AI Agent → Relevant Business / Architecture Rules → SQL Generation → Database
This creates an interesting separation of responsibilities:
🤖 AI Agent — understands the user’s intent and generates the required actions
📋 Rules Layer — defines how those actions must be performed in a specific environment
🗄️ Database — executes the controlled SQL operations
For me, this is an important step toward moving an AI SQL Agent from a simple natural-language-to-SQL demo toward an assistant that can operate within defined enterprise standards and business processes.
It also opens the door to managing rules centrally, activating or deactivating them, adding new rules without changing the Agent’s core code, and eventually applying different rule sets to different environments or use cases.
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