I Built a Safety Layer Between AI Agents and Postgres — Here's Why Raw SQL Access Is a Trap

Let me describe a scenario that should make any developer uncomfortable. You give an AI agent access to your Postgres database. A user asks it something like "clean up the old test data." The agent generates DELETE FROM users WHERE created_at < '2024-01-01' — and runs it. No preview. No confirmation. Just gone. This isn't hypothetical. It's the default behavior of almost every AI + database setup being built right now. I built EnginiQ to fix this. The Problem with "Just Give the LLM Your Database URL" When I started experimenting with AI agents doing real database work, the naive setup looked like this: Pass the database URL to the agent Let it generate SQL Run it It works — until it doesn't. The failure modes aren't always dramatic. Sometimes it's a TRUNCATE on the wrong table. Sometimes it's an ALTER TABLE that takes a lock mid-traffic. Sometimes it's a well-intentioned DELETE missing a WHERE clause. The problem isn't that LLMs write bad SQL. They're actually quite good at it. The pr