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.

翻译：基于大语言模型的智能体正进入受监管行业，在这些领域中自动化执行判断密集型质量管理流程。我们认为，这些领域内已嵌入的符号化结构（包括法规、类型化流程模型及合规约束）不应仅被视为外部监控机制，而应作为塑造智能体决策与行为的核心架构组件。我们提出"构架合规"作为基于护栏监控的补充范式：这是一种防止控制流违规的结构基础，同时护栏对捕获语义错误仍至关重要。我们识别出基础层与能力层的一系列结构化神经符号研究挑战，并表明协同应对这些挑战能够实现构架合规。我们呼吁神经符号社区将受监管流程自动化视为高影响力研究领域加以投入。