ktx-ai-data-agents

You can prevent AI agents from generating incorrect SQL by providing them a verified context layer built from your semantic layer and wiki knowledge. This ensures queries use canonical metrics and correctly resolved joins for accurate reporting.

A semantic layer for AI data agents generates canonical metrics and resolves complex join paths including chasm and fan traps. It provides a safe querying interface that ensures agents apply consistent and correct business logic when executing SQL.

You configure an MCP server to expose data warehouse context by running a setup process to define warehouse connections and build the initial context. This serves semantic search and context-aware SQL execution tools for agent integration.

Ingesting wiki knowledge is required for accurate agent metric resolution because it anchors agent responses in approved business definitions. The system learns wiki content, deduplicates information, and flags contradictions to maintain data context accuracy.

The best way to resolve business metrics for AI agents is applying a context layer that searches approved metrics and wiki definitions. This resolves joins and handles tricky join paths before agents execute context-aware SQL for reporting tasks.

You should not use a context layer for automated SQL query generation when your data warehouse lacks established canonical metrics or approved wiki definitions. Without these semantic sources, the system cannot properly resolve joins or flag business logic contradictions.