Executable Analytics Context Layer That Compiles dbt, Looker, and Notion Into Agent-Ready Semantics
Ktx ingests definitions already living in dbt, Looker, Metabase, and Notion, auto-detects metric semantics, and emits a file-based context layer that data agents can execute against, an approach the maintainers contrast with hand-authored semantic layers like Wren and Cube. Text-to-SQL fails in production because business context is scattered, and every data team gluing an agent to the warehouse rediscovers this. Auto-compiled context is the missing infrastructure.
Problem Statement
A data team pointing an agent at Snowflake today either accepts wrong joins and misdefined metrics or hand-authors MDL for a semantic layer engine, a months-long project that drifts from dbt the day it ships. Definitions already exist in dbt YAML, Looker LookML, Metabase models, and Notion wikis, but nothing compiles them into one executable context, so each agent vendor re-extracts a partial copy.
The Idea
A context compilation layer for data teams deploying warehouse agents who need their existing dbt, BI, and wiki definitions turned into executable semantics without hand-authoring a second model.
Why Now
Enterprise text-to-SQL deployments through 2025 hit accuracy walls traced to missing business context, and the response, hand-written semantic layers, doubles modeling work nobody staffs. The pieces needed for automatic compilation, mature dbt adoption and LLM-based definition extraction, both exist as of 2026, and Spider 2 gives the category a public accuracy scoreboard.
Target User
Analytics engineers and data platform leads deploying AI agents over cloud warehouses
Target Market
Data and analytics infrastructure for AI agents
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