AI Governance Must Be Runtime and Deterministic

AI Governance Must Be Runtime and Deterministic

I follow the AI governance news closely, and recently I have been hearing a lot of chatter about AI governance harnesses. I think the enterprise industry is slowly waking up and realizing that Large Language Models (LLMs) need a cage.

I actually like the idea of thinking about AI governance in terms of harnesses. SAFi is technically a harness itself, but the way the rest of the industry is approaching the problem is completely backward.

A Different Approach: SAFi

Take a tool like Claude Code. It is an agentic harness, it has a software layer built around the LLM to give it tools, memory, and execution capability. But in that setup, the LLM is still the star of the show. It executes and makes decisions on its own.

SAFi, on the other hand, makes the LLM a substrate of its thinking process. In SAFi, the LLM is just another step in the execution pipeline. The LLM is not in charge; it is simply a component in the loop.

The SAFi Execution Loop

Because the LLM is the reasoning module, it can propose an action. But that action must first be approved by an independent module. That approval module is pure Python, it is completely blind. It cannot reason; it just executes based on deterministic rules.

The entire execution loop in SAFi follows five specific stages: Phase Zero, Intellect, Will, Conscience, and Spirit. Out of these five slots, only the Intellect and Conscience invoke an LLM because they actually require semantic reasoning. The rest of the loop is entirely deterministic Python code.

Predictability through Determinism

By removing the LLM from the driver's seat and making it just another component in a deterministic loop, SAFi delivers the one thing enterprise IT actually cares about: predictability