Provably Auditable and Safe LLM Agents from Human-Authored Ontologies

Researchers introduce Agentic Redux, an LLM agent architecture that guarantees semantic correctness and auditability using typed lambda calculus, paired with a new Ontology-First Agent Design methodology. The framework is demonstrated in healthcare billing compliance and security vulnerability disclosure domains, offering production-grade implementations with provable safety guarantees.

Agentic Redux addresses a critical gap in autonomous AI systems: the need for verifiable correctness and complete auditability in high-stakes domains. By grounding LLM agents in formal mathematical logic and append-only ledgers, this architecture eliminates the black-box problem that has plagued enterprise AI adoption. The approach is particularly significant for regulated industries where decision trails must withstand scrutiny from compliance officers, auditors, and legal teams.

The Ontology-First Agent Design methodology represents a meaningful shift in how AI systems should be architected for complex domains. Rather than forcing domain experts to adapt to AI frameworks, this approach begins with human experts formalizing domain knowledge using Basic Formal Ontology, then derives agent roles and responsibilities from this structured representation. This inverts the typical pipeline and reduces misalignment between AI behavior and domain requirements.

For enterprises operating in healthcare, finance, and security sectors, Agentic Redux offers tangible advantages. Healthcare billing compliance is notoriously complex, with millions of dollars at stake through coding errors and billing disputes. Security vulnerability disclosure involves equally high stakes—incorrect or incomplete disclosure can expose organizations to legal liability and reputational damage. By proving that agent decisions are semantically correct and fully traceable, this framework could accelerate AI deployment in industries previously hesitant to automate critical workflows.

The availability of working production code for both demonstrated domains suggests the researchers have moved beyond theoretical contributions. Organizations facing audit requirements or seeking to implement AI with verifiable safety guarantees should monitor this framework's development and adoption trajectory.

• →Agentic Redux uses typed lambda calculus to mathematically prove LLM agent decisions are semantically correct and fully auditable.
• →Ontology-First Agent Design methodology starts with human experts formalizing domain knowledge before deploying AI agents.
• →Framework demonstrates production-grade implementations in healthcare billing and security vulnerability disclosure.
• →Append-only ledger recording enables complete decision traceability for compliance and regulatory requirements.
• →Architecture addresses enterprise adoption barriers by eliminating black-box decision-making in high-stakes domains.