做监管流程自动化的团队会发现，将符号结构嵌入智能体架构比事后监控更可靠，建议研究LLM智能体的开发者关注这一新范式。
该论文提出，在受监管行业中，基于LLM的智能体应利用领域内已有的符号结构（如法规、流程模型和合规约束）作为核心架构组件，而非仅作为外部监控。作者提出“合规即构建”范式，与传统的护栏式监控互补，从结构上防止控制流违规，同时保留护栏用于捕获语义错误。论文识别了基础和能力层面的神经符号研究挑战，并呼吁神经符号社区关注这一高影响力领域。
Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda
LLM-based agents are entering regulated industries where they automate judgment intensive quality management processes. We argue that symbolic structures already embedded in these domains, including regulations, typed process models, and compliance constraints, should be treated not merely as external monitoring mechanisms but as core architectural components that shape the agent's decision-making and behavior. We propose compliance-by-construction as a complementary paradigm to guardrail-based monitoring: a structural foundation that prevents control-flow violations, while guardrails remain essential for catching semantic errors. We identify a structured set of neuro-symbolic research challenges on foundational and capability level and show that addressing them jointly enables compliance-by-construction. We call on the neuro-symbolic community to engage with regulated process automation as a high impact research domain.