dbt Labs open-sourced a YAML-based charting language that lets AI agents generate dashboards as code instead of UI-bound reports, solving the friction between messy code generation and restrictive BI tool interfaces. The accompanying dbtCharts.com platform adds hosting, access control, and conversational analytics on top of the open standard.

• Current AI-generated reports create sprawling file structures across multiple languages, making audits slow and token-expensive; dbt Charts consolidates everything into a single readable YAML file that agents can modify efficiently
• Charts defined in code live in Git alongside dbt models, so schema changes and chart updates ship together in one CI run and fail before reaching production
• The language includes 1,100+ config options across 16 chart types with cascading styles and inheritance, designed to look intentionally crafted rather than dashboard-grid generic

dbt-doctor scans dbt projects for maintainability issues like missing documentation, weak test coverage, schema drift risks, and DAG problems. It generates a health score (0–100) and can run locally, in pre-commit hooks, or as a GitHub Actions quality gate.

• Detects 122 rules across 9 categories including missing docs/tests, stale models, naming convention violations, and governance gaps
• Integrates into CI/CD with GitHub Actions, outputs sticky PR comments, and can fail builds based on error/warning thresholds
• Configurable via presets (default/strict/enterprise), inline suppressions, and .dbt-doctor config file; respects .gitignore and .sqlfluff rules

Claire Gouze lays out a practical process to build and test a context layer your data agents can trust. She shows how to start small with dbt docs and markdown files, write evals, clean up data models, and iterate until you reach around 90% reliability.

• Adding query logs and profiling alone got the agent stuck around 40% accuracy; reworking the data model and documentation pushed it to 90%.
• Treat context like code: store it in files/markdown, version in Git, add CI/CD evals, and iterate on failure cases rather than plugging agents directly into raw databases.
• Start small—10 to 20 core tables with existing dbt docs—rather than trying to cover the whole warehouse at once.
• Evals should come from real BI query history or autogenerated question sets, reviewed by teams and tracked over time to build trust in the agent.

SQLBuild plugs into an existing dbt project to fingerprint models, skip unchanged work, and clone production tables in development instead of rebuilding them. It stores all state in append-only warehouse tables, adds ingestion and Python nodes, and offers built-in testing, audits, and incremental processing with no external services.

• Wraps your existing dbt project (no file edits, no new models) by fingerprinting models/seeds/functions/Python nodes and skipping anything unchanged, even cloning unchanged prod tables instead of rebuilding them.
• All state stored as append-only tables in the warehouse itself—no external service, manifest files, or state machine required.
• Runs audits before data lands: full builds stage until checks pass, incremental models validate each microbatch before merging.
• One pip install gets you a runnable sample project, and it already supports DuckDB, MotherDuck, Snowflake, BigQuery, Databricks, Postgres, and SQL Server.

Docglow replaces built-in dbt docs with an interactive single-page app that works out of the box with any compiled dbt Core project. It generates a static site featuring unlimited models, column-level lineage, project health scores, full-text search, AI-powered chat, and dark mode with zero configuration.

• Two commands (pip install + generate) turn compiled dbt artifacts into a static docs site with no dbt Cloud or backend required.
• Includes column-level lineage tracing with transformation labels (direct/derived/aggregated) and a health score for docs/test/description coverage.
• Can gate CI pipelines with --fail-under and cut large-project payloads by up to 60% by skipping column lineage or slimming output.
• AI chat and an MCP server let editors query project metadata directly, with chat streaming via a user-supplied Anthropic key rather than shipping it.