A 3-line wrapper that enforces deterministic security for AI agents
Deterministic policy checks beat LLM-as-judge for agent security, no token burn.
Give your AI agents database access without the risk.
Policy enforcement at the ORM layer beats prompt-layer security for agent database access.
Developers building AI agents with database access
LangChain Tools · MCP Servers · Vellum
Jk, The real reason has always been to enforce access policy, do easy CRUD interfaces and so on.
Now with all these agents being built, I figured they should not be making SQL calls directly (just like junior eng :P).
So i present ormAI, this is the official blurb.
OrmAI wraps your existing ORM models in a policy-enforced runtime. Your agents get typed tools for querying and writing data — while you keep control over what they can see and do. No raw SQL. No prompt injection into your database. Just safe, auditable, tenant-scoped database tools.
I did benchmark it with Text to SQL tools, and for obvious reasons this did not do badly at all.
Check it out, write more AI agents.
Deterministic policy checks beat LLM-as-judge for agent security, no token burn.
Eight-layer governance pipeline for agents when LangChain just executes blindly.
Intercepts tool calls before execution to block dangerous actions like DB deletes.
Zero-trust governance for AI agents before they execute shell, file, or database actions with full audit trails.
Type-system-enforced governance loop prevents agents from bypassing policy without code changes.
Stops wallet-draining AI agents with rule-based guards, addresses real emerging pain.