Build a Safe SQL Agent Without Giving the Model SQL Access
The fastest way to connect an AI agent to application data is often a generic SQL tool. Give the model a schema, accept a SQL string, run it, and return the rows.
That is also where a prototype can quietly become a production security and maintenance problem.
The model must understand physical table names, joins, nullable columns, tenant boundaries, authorization rules, and mutation policy. Database details become part of the prompt contract. A schema change can invalidate that contract, and a broadly capable SQL tool exposes much more authority than most business tasks require.
TeaQL takes a different approach:
Don't give your AI agent unrestricted SQL. Give it a typed business language.
The open-source @teaql/ai-sdk
adapter converts an explicit allowlist of business capabilities into native
Vercel AI SDK tools. The model sees operations such as
findSchoolsMissingContact and updateSchoolContactPhone. The server keeps the
TeaQL context, runtime resources, authorization state, credentials, and
internal failures.