SAFi Framework Proposes Deterministic Control Layer to Govern LLM Behavior

A developer perspective published on DEV Community argues that mainstream AI governance tools are taking the wrong approach by keeping LLMs in control of their own execution. The author contrasts tools like Claude Code, where the LLM drives decisions autonomously, with SAFi, a framework that treats the LLM as just one component within a deterministic pipeline. In SAFi, any action proposed by the LLM must be approved by a separate, rule-based Python module that cannot reason independently. The execution loop consists of five stages, with LLMs invoked in only two of them where semantic reasoning is genuinely needed. The author contends that this architecture delivers the predictability that enterprise IT environments require from AI systems.