Introduction

Data agents can write SQL. The hard part is making sure they write the SQL your analytics team would have written.

KTX is the agent-native context layer for analytics engineering. At its core is a semantic layer: YAML sources that define tables, columns, measures, joins, grain, filters, segments, and computed fields. Around that core, KTX adds the context analytics agents need to work safely: warehouse scans, knowledge pages, ingestion from existing tools, provenance, validation, and MCP access.

KTX projects are plain files — YAML, Markdown, and SQLite — that you commit to git and review in PRs, just like dbt models. Agents can read them, edit them, validate them, query through them, and leave behind a diff your team can review.

Who KTX is for

KTX is built for analytics engineers and data teams who want data agents to work on real analytics systems, not just generate one-off SQL.

Use KTX when you want agents to:

• Generate SQL from approved measures, dimensions, and joins
• Repair or extend semantic definitions through reviewable git diffs
• Explain where a metric definition came from and what business rules shape it
• Use warehouse scans and relationship evidence instead of guessing join paths
• Work alongside dbt, LookML, MetricFlow, Looker, Metabase, Notion, and BI platforms
• Work with warehouses like PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, or SQL Server

If you've ever watched an agent confidently generate a query that joins on the wrong key or invents a metric that doesn't exist, KTX is the fix.

What KTX gives agents

• A semantic layer they can edit — plain YAML sources with measures, dimensions, joins, grain, segments, filters, and computed columns
• Safe query planning — grain-aware SQL generation, fan-out detection, chasm-trap handling, and dialect transpilation
• Business context — Markdown knowledge pages for definitions, rules, exceptions, and data quality notes
• Schema evidence — warehouse scans with table metadata, column stats, constraints, and inferred relationships
• Provenance — ingest transcripts and replay metadata that explain where context came from and why it changed
• An agent-facing API — MCP and CLI tools for reading, writing, validating, searching, and querying context

How these docs are organized

Next steps

• Get hands-on — follow the Quickstart to set up KTX with your own database in under 10 minutes.
• Understand the theory — read The Context Layer to learn why schema access alone breaks on real analytics and how KTX addresses it.