Machine Learning–Augmented Neurosymbolic Agenticops Framework for Runtime Verification and Enforcement of Standard Operating Procedures

Abstract

I remember the first time I saw an AI agent go off the rails during a demo at the ISBM College Hackathon—it was supposed to handle a simple refund process but ended up “approving” a fake transaction because it lost track midway through the chat. Moments like that highlight the real issue: as Generative AI shifts from just chatting to actually acting in the world with “Agentic” systems, enterprises face this weird reliability crunch. LLMs are amazing at reasoning, sure, but they’re plagued by this shaky unpredictability I call “Logic Drift”—basically, they start veering away from the rules as conversations drag on.
That’s why, in this work, I put together “LogicGuard,” a neurosymbolic setup aimed at fixing these slip-ups. It basically layers a solid, rule-based checker around the fuzzy AI brain, using Linear Temporal Logic on Finite Traces (LTLf) to keep things in line. We turn everyday procedure docs into these neat Deterministic Finite Automata (DFA) machines that enforce the rules no matter what. The whole thing breaks down into three parts: a compiler for the rules, a prober to link words to logic symbols, and a gatekeeper that says yes or no to actions.
Testing it out in finance, auth, and logistics scenarios, Logic-Guard held steady at about 95% reliability on those marathon tasks where plain agents tanked to under 50%. It edged out four other safety tools by roughly double in handling tricky attacks. That said, we still hit a 5% snag from fuzzy symbol match-ing—I’ll dive into ablations to break down that neurosymbolic headache.