EBRB–LLM Hybrid Architectures for Trustworthy Agentic AI: Vision, Opportunities, and Challenges

Abstract

Agentic AI based on large language models (LLMs) is rapidly evolving from static chatbots to autonomous systems that plan, act, and interact with tools in open-ended environments. However, current LLM agents lack calibrated uncertainty, robust safety mechanisms, and faithful explanations, making them ill-suited for safety-critical settings such as healthcare, finance, cybersecurity, and industrial operations. Extended Belief Rule Bases (EBRB) are representative examples of interpretable, rule-based probabilistic reasoning frameworks with explicit representation of belief and ignorance, and have been successfully applied in complex decision problems without suffering from the rule explosion that affects traditional rule-based approaches. This position paper argues that EBRB could serve as a core safety and reasoning governor within LLM-based agentic pipelines, yielding hybrid systems to better align agentic AI systems with emerging regulatory and ethical requirements for Trustworthy AI (TAI) in high-risk settings. We outline: (i) a conceptual architecture integrating LLMs with EBRB in agentic workflows; (ii) the mapping from this architecture to TAI dimensions including transparency, uncertainty, safety, fairness, and auditability; (iii) concrete opportunities across healthcare, finance, cybersecurity, and industrial safety; and (iv) a research agenda highlighting open challenges in scalable rule induction, co-adaptation between LLMs and EBRB, and evaluation of hybrid agents. Our goal is not to present empirical benchmarks, but to articulate a vision and roadmap for EBRB-enhanced agentic AI that is both powerful and trustworthy.