Schema-guard, stop AI agents from inventing column names in SQL

Your AI agent stops inventing column names.

Coding agents write SQL against the schema they think you have, based on your README, an old query, or a naming convention. Then it fails in CI, in a dashboard, or at 2am. Snowflake's own developer blog ran a whole post on this in September 2026 (My coding agent won't stop hallucinating table columns).

schema-guard keeps a snapshot of your real tables and columns in the repo (names and types only, no data, no credentials) and checks the agent's SQL against it before it runs or lands in a file:

schema-guard: this SQL names things that are not in the schema snapshot (.schema-guard/schema.json, taken 2026-09-29T01:35:07Z):
- `analytics.customers` has no column `country`. Did you mean: `country_iso2`?
- `analytics.orders` has no column `customer_id`. Did you mean: `cust_id`, `order_id`?
- `analytics.customers` has no column `id`. Did you mean: `cust_id`?
Fix the names and try again. ...

That's a real denial from the eval below: Claude Haiku 4.5 writing models/revenue_by_country.sql from a README that describes last year's schema. The agent reads the denial, fixes its SQL and moves on. You never see the broken version.

Setup. An analytics repo whose README describes an older schema (customer_id, created_at, country), plus two up-to-date models that use a few of the real names. The agent can read and write files but can't reach the warehouse. That's the situation in Snowflake's post: the agent has the repo, not the account. Each request asks for a new SQL model, and afterwards the grader runs every file the agent wrote against the real DuckDB warehouse. 4 requests × 3 arms × 3 runs, on Claude Code.

What that means:

• Without a snapshot, neither model wrote one working file (0 of 24). Both trusted the README, and even when they copied real names from the existing models they mixed them with stale ones.
• With the snapshot, every file ran (48 of 48). The 2 wrong answers are logic errors, not names: Haiku started weeks on Sunday, and Sonnet counted the last days of 2025 in the first week.
• The hook is the safety net for agents that don't go looking. Haiku was denied in 10 of its 12 hook runs and fixed the names on the first retry every time. Sonnet found.schema-guard/schema.json by itself and was never denied. A one-line rule gets the same result if the agent follows it; the hook doesn't depend on that, and it also covers ad-hoc queries and MCP tools.
• Cost: the hook arm cost about the same as baseline ($0.65 vs $0.58 for 12 Haiku runs; $1.61 vs $1.61 for Sonnet).

Be skeptical of this: the world is small and synthetic, and the stale README is designed in (docs drift is normal, but I chose how far). There are 3 runs per cell. Two grader references were added after I read runs: "net revenue" net of refunds (it changed 2 Haiku grades, one rule run and one hook run), and listing all 52 weeks with zeros (5 Sonnet grades). Both are disclosed in evals/scenarios.py, and every run's SQL is in evals/results/. Rerun it: cd evals && python run_eval.py --model <model> --runs 3.

pip install "schema-guard[duckdb] @ git+https://github.com/idk-arsh/schema-guard"
schema-guard snapshot --dbt target        # or --duckdb, --bigquery, --snowflake, --databricks, --url, --ddl, --csv
git add .schema-guard/schema.json

Then use it however your team works. All of these read the same snapshot:

Several files in .schema-guard/ are merged, so one repo can cover more than one warehouse. The person taking the snapshot needs warehouse access once; the agent never does.

I ran the Databricks reader against a fresh Databricks Free Edition workspace to prove the Unity Catalog path works end to end.

$env:DATABRICKS_HOST = "<workspace>.cloud.databricks.com"
$env:DATABRICKS_HTTP_PATH = "/sql/1.0/warehouses/<id>"
$env:DATABRICKS_TOKEN = "<token>"

python -m schema_guard.cli snapshot --databricks samples -o .schema-guard/databricks-samples.json

python -m schema_guard.cli check "SELECT customerid, first_name FROM samples.bakehouse.sales_customers LIMIT 10"

python -m schema_guard.cli check "SELECT customer_id, first_name FROM samples.bakehouse.sales_customers LIMIT 10"

The snapshot came back in under 30 seconds on a cold warehouse. The reader pulls from information_schema.columns, so any Unity Catalog you can read works the same way.

• Shell commands:bq query ,snowsql ,snow sql ,psql ,duckdb ,sqlite3 ,databricks ,spark-sql ,mysql ,trino , SQL passed to scripts (python run_sql.py "..." ,python -c "...sql..." ), heredocs and-f file.sql .
• Files: every.sql the agent writes or edits. dbt{{ ref() }} and{{ source() }} are resolved to real tables. On an edit, only problems the editadds are reported, so old debt in a file doesn't block new work.
• MCP tools: any tool with asql /query /statement argument (Snowflake, Databricks, BigQuery, Postgres MCP servers).
• Resolution: CTEs, subqueries, correlated subqueries, aliases,USING , set operations, CTAS and temp tables created earlier in the same script, INSERT column lists, UPDATE SET, DELETE WHERE. Parsing is bysqlglot , so 20+ dialects.

A false block costs more trust than a missed one, so it says nothing when it can't be sure:

• SQL it can't parse, Jinja beyond ref/source/config, sources it can't see into (UNNEST, LATERAL, table functions, PIVOT), SELECT * from a table it doesn't know, struct and JSON field access.
• Tables from a database the snapshot doesn't cover (unless the name is a near miss of one it does).
• Stale snapshot: if the agent sends the exact same SQL again after a denial, it goes through. A new column can slow the agent down once but never lock it out. Refresh withschema-guard snapshot , and put--check in CI.

A guard that blocks valid SQL gets uninstalled, so this matters more than the catch rate.

Every valid query is human-written gold SQL that runs on its database, so any finding would be a false block. The planted mistakes swap one real name for a wrong one the way agents get it wrong (a column from another table, _id / plural / _name variants, singular vs plural table names). I fixed 3 checker bugs that defog exposed, so treat its numbers as training numbers; Spider is the honest one. Its 2 misses are inside correlated subqueries, where the checker deliberately gives the benefit of the doubt. Run them: python evals/benchmark_spider.py, python evals/benchmark_defog.py (needs pip install defog-data).

• It checks names, not meaning. SUM(gross_amount) when you wantednet_amount passes.
• Dynamic SQL built from string pieces in application code isn't seen.
• The snapshot is only as fresh as the last schema-guard snapshot .
• The Snowflake and BigQuery snapshot readers are unit-tested on their output format, not yet run against live accounts. The Databricks reader was run live on Free Edition on 2026-10-04 (see below). If you run one, tell me how it went .

Part of a set of small, measured tools for AI agents working on data: data-agent-rules (rules + safety hooks, cost checks, masked previews), show-your-sql (every number in the answer traced to a query result), data-test-guard (agents can't delete or loosen tests to go green).

MIT licensed. Using it? Open a "We use this" issue or add a line to ADOPTERS.md. False blocks are the bug I most want to hear about.