The rapid evolution of autonomous, agentic artificial intelligence within financial services has introduced an existential architectural crisis: large language models (LLMs) are probabilistic, non-deterministic systems operating in domains that demand absolute, mathematically verifiable compliance guarantees. Existing guardrail solutions -- including NVIDIA NeMo Guardrails and Guardrails AI -- rely on probabilistic classifiers and syntactic validators that are fundamentally inadequate for enforcing complex multi-variable regulatory constraints mandated by the SEC, FINRA, and OCC. This paper presents the Lean-Agent Protocol, a formal-verification-based AI guardrail platform that leverages the Aristotle neural-symbolic model developed by Harmonic AI to auto-formalize institutional policies into Lean 4 code. Every proposed agentic action is treated as a mathematical conjecture: execution is permitted if and only if the Lean 4 kernel proves that the action satisfies pre-compiled regulatory axioms. This architecture provides cryptographic-level compliance certainty at microsecond latency, directly satisfying SEC Rule 15c3-5, OCC Bulletin 2011-12, FINRA Rule 3110, and CFPB explainability mandates. A three-phase implementation roadmap from shadow verification through enterprise-scale deployment is provided.

翻译：自主式智能体人工智能在金融服务领域的快速发展引发了根本性的架构危机：大语言模型作为概率性、非确定性系统，运行在需要绝对且数学可验证的合规保证的领域中。现有的护栏解决方案（包括NVIDIA NeMo Guardrails和Guardrails AI）依赖于概率分类器和语法验证器，根本不足以执行SEC、FINRA和OCC所要求的复杂多变量监管约束。本文提出了基于形式化验证的AI护栏平台——Lean-Agent协议，该平台利用Harmonic AI开发的亚里士多德神经符号模型，将机构策略自动形式化为Lean 4代码。每个提出的智能体动作都被视为一个数学猜想：当且仅当Lean 4内核证明该动作满足预编译的监管公理时，才允许执行。该架构在微秒级延迟下提供密码学级别的合规确定性，直接满足SEC Rule 15c3-5、OCC Bulletin 2011-12、FINRA Rule 3110以及CFPB的可解释性要求。本文还提供了从影子验证到企业级部署的三阶段实施路线图。