--- title: Introduction description: How KTX gives analytics agents trusted context for warehouse work. --- import { ProductMechanics } from "@/components/product-mechanics";

Make analytics context usable by agents

{'KTX turns warehouse metadata, semantic definitions, and business knowledge into reviewable project files that agents can use while planning, querying, and updating analytics work.'}

## What agents can do with KTX 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 it when agents need to: - **Generate SQL** from approved measures, dimensions, joins, and filters - **Explain provenance** with wiki context and warehouse evidence - **Repair context** through reviewable YAML and Markdown diffs - **Work alongside** dbt, LookML, MetricFlow, Looker, Metabase, and warehouses KTX works with SQLite, PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, and SQL Server. ## Read next Set up KTX and build your first context in under 10 minutes. Hands-on workflows for scanning, ingesting, writing, and serving. Edit semantic-layer YAML and wiki Markdown safely. Complete flag and subcommand reference for every KTX command. ## Agent usage notes | Agent task | Read next | |------------|-----------| | Discover machine-readable docs | [AI Resources](/docs/ai-resources) | | Learn how a coding assistant should approach KTX | [Agent Quickstart](/docs/ai-resources/agent-quickstart) | | Set up a new KTX project | [Quickstart](/docs/getting-started/quickstart) | | Explain what problem KTX solves | [The Context Layer](/docs/concepts/the-context-layer) | | Scan a database and ingest metadata | [Building Context](/docs/guides/building-context) | | Edit semantic sources or wiki pages | [Writing Context](/docs/guides/writing-context) | | Look up exact command flags | [CLI Reference](/docs/cli-reference/ktx-setup) |