Building Proactive AI Agent Governance: Policy Engines in the Request Pipeline

It’s becoming increasingly clear to me that the world needs a governance system for complex, highly autonomous AI systems such as self-driving vehicles. But looking at current governance systems, all of them do one thing in common: they react after something has already happened, and they record everything that has occurred in a log file, with the vague hope that perhaps someone will read the log file and perhaps identify a pattern. This post-reactive approach to what can be called a “regulatory bank” is akin to having a bank that records every transaction but doesn’t have any preventative controls in place to stop a fraud transaction from occurring in the first place, with the knowledge that you’ll only find out something has gone wrong after it has already happened. I wanted to give you a preview of a new system we are building at Mnemom. We have been playing with the idea of shifting governance earlier in the request pipeline, before an agent would actually act. The Monitoring vs Go