I've been building AI-powered features for a while now, and the hardest conversations I have with my team are never about which model to use. They're always about the same thing: what is this system actually allowed to do, and how do we prove it?
That question pushed me to build PolicyAware - an open source Python control plane that sits in front of your models, tools, and retrieval systems. Before I explain what it does, I want to walk through why the tools most teams reach for first - guardrails, AI gateways, and model routers - are genuinely useful but leave a critical gap wide open.
The landscape right now
If you search for "AI safety" or "LLM governance" you will find three categories of tools coming up again and again:
Guardrail libraries - validate prompts and outputs against safety rules
