Build Database Agents That Get Smarter With Every Query (n8n)

Demonstrates building self-improving database agents that answer natural-language queries against tabular data, comparing five interaction patterns and security trade-offs.
– MCP and vector-memory: capture and reuse successful SQL queries to reduce hallucinations.
– Direct Postgres, hardcoded schema, and flattened database views: reduce query complexity and context bloat.
– Parameterized queries and role-level security: enforce least-privilege, prevent injection, and enable deterministic access.

Quotes:

If you ask your agent a simple question such as, ‘What was my revenue last month?’ it will likely retrieve isolated chunks out of context.

This is one of the most powerful yet underrated retrieval methods to use with your AI agents.

If the question cannot be answered from the database or if a query returns no rows reply with sorry I don’t know.