Three Open-Source Tools Aim to Formally Prove AI Agent Rules Work on Every Input

A developer has published three open-source projects designed to bring mathematical certainty to AI agent governance, addressing the gap between probabilistic LLM outputs and deterministic rule enforcement. The core tool, ERDL (Entity-Rule Definition Language), lets engineers define agent behavior rules in plain YAML with a fixed semantic tree, precise decimal arithmetic, and three-valued logic to prevent ambiguous outcomes. A second layer, erdl-vectors, provides 301 frozen cross-implementation test vectors to verify that independent rule engines produce identical results byte-for-byte. A third component, erdl-formal, goes further by attempting to prove that rules hold for every possible input, not just those covered by unit tests. Together, the tools aim to shift the claim of 'deterministic AI governance' from a marketing assertion to a verifiable, auditable guarantee.