Data Governance for AI Agents via YAML and DDD

A YAML-first tool to teach AI agents business domains, metrics, and rules before SQL generation.

Data Governance for AI Agents via YAML and DDD

This tool provides a structured approach to governing AI agents by defining business domains, metrics, and rules using YAML and Domain-Driven Design (DDD) principles. It enables developers to explicitly teach AI agents the context and constraints of their operational environment before they generate SQL queries or perform other data-intensive tasks. This ensures that AI agent actions are aligned with business objectives and data integrity standards.

What it Does

The core functionality is to translate human-readable YAML definitions into actionable configurations for AI agents. These definitions encapsulate the business domain's structure, key performance indicators (KPIs), and governing rules. By processing these YAML files, the tool empowers AI agents to understand the nuances of the data they interact with, leading to more accurate, relevant, and compliant data operations. It acts as a bridge between business logic and AI agent execution, preventing common issues like data misuse or incorrect query generation.

Key Features

YAML-First Configuration: All domain knowledge, metrics, and rules are defined in clear, version-controlled YAML files.
Domain-Driven Design Integration: Leverages DDD concepts to model business domains effectively, fostering better understanding and communication.
Pre-SQL Generation Governance: Enforces rules and constraints before AI agents generate SQL, reducing the risk of errors and non-compliance.
Metric Definition: Allows for the explicit definition of business metrics that AI agents should track or utilize.
Rule Enforcement: Enables the specification of data validation and operational rules that AI agents must adhere to.

Who it's For

This tool is designed for AI developers, data engineers, and architects working on building and deploying AI agents that interact with sensitive or complex data environments. It is particularly beneficial for teams that need to ensure data quality, regulatory compliance, and alignment of AI agent behavior with business strategy. If you are building AI agents that require a deep understanding of business context and data governance, this tool offers a robust framework for achieving those goals.