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Agentic Redux offers a formal verification approach to LLM agents by grounding them in typed lambda calculus and human-authored ontologies, ensuring semantic correctness and linear auditability for high-stakes domains like healthcare and security. This methodology shifts agent reliability from probabilistic outputs to provable guarantees, addressing critical needs for accountability in regulated industries. The accompanying Ontology-First design framework provides a structured path for integrating human expertise with automated reasoning to define clear agent roles.
Provably Auditable and Safe LLM Agents from Human-Authored Ontologies
arXiv:2606.04903v1 Announce Type: cross Abstract: 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.
Abstract: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.
Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Programming Languages (cs.PL)
arXiv:2606.04903 [cs.LO]
(or arXiv:2606.04903v1 [cs.LO] for this version)
https://doi.org/10.48550/arXiv.2606.04903 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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