We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem domains that require linear auditability. Using the typed lambda calculus, we prove that, run on appropriate domains, Agentic Redux executions are semantically guaranteed to be correct, with all decisions recorded in an append-only ledger. We present two production-grade appropriate domains, in healthcare billing compliance, and security vulnerability disclosure. Working code for Agentic Redux run on both domains is available in a supporting code repository. We also introduce Ontology-First Agent Design, a methodology for creation of agent frameworks on a problem domain, in which a human expert ontologizes the problem domain with Basic Formal Ontology, and then assigns an LLM to derive roles that agents and humans-in-the-loop can fill, in order to work the problems in the domain.

翻译：我们提出了LLM智能体架构Agentic Redux，专为需要线性可审计性的非平凡问题域而设计。利用类型化λ演算，我们证明了在适当域上运行时，Agentic Redux的执行在语义上保证是正确的，且所有决策都记录在仅追加的账本中。我们提出了两个生产级应用域：医疗账单合规性与安全漏洞披露。两个域的Agentic Redux可运行代码均在配套代码仓库中提供。我们还引入了本体优先智能体设计方法，这是一种基于问题域创建智能体框架的范式：人类专家首先利用基本形式本体对问题域进行本体化，随后指定LLM推导出智能体和人机协同系统可承担的角色，以解决该领域内的具体问题。