Unleashing AI Agents In Your Database
As organizations look to integrate AI agents, the blast radius for mistakes becomes much larger when you connect them to databases.
To mitigate risk, you should:
Tightly scope roles to read only for the data warehouse
Never assign them access to production databases
Add prompt engineering to your chat interface to prevent any harmful commands (drop, create or replace, alter)
Create agent objects only in an agent database, and/or in a separate server isolated server instance
This could look like: transactional db → data warehouse → duckdb database for agents
Having the agents only operate at the duckdb boundary will full access allows for rapid experimentation and testing
- If agents create successful queries, you could “compile” those queries back into the data warehouse’s sql dialect.
While this is interesting to think about, I hesitate to roll this out for various reasons.
Very rarely is sql code generation the bottleneck to analytics, but rather, gathering requirements, data modeling and visualizations.
Without proper context, the agents will just be creating a bunch of incorrect metrics.
The cost of LLMs + data warehouse compute needs to be strictly monitored if this is deployed.
You are one wrong grant command to AI agent away from destroying your database.
AI agents might have a place in the future for databases if their cost and risks are carefully managed.