Large language model agents suffer from fundamental architectural problems: entangled reasoning and execution, memory volatility, and uncontrolled action sequences. We introduce Structured Cognitive Loop (SCL), a modular architecture that explicitly separates agent cognition into five phases: Retrieval, Cognition, Control, Action, and Memory (R-CCAM). Soft Symbolic Control constitutes a dedicated governance layer within SCL, applying symbolic constraints to probabilistic inference while preserving the flexibility of neural reasoning and restoring the explainability and controllability of classical symbolic systems. Through empirical validation on multi-step conditional reasoning tasks, we demonstrate that SCL achieves zero policy violations, eliminates redundant tool calls, and maintains complete decision traceability. These results address critical gaps in existing frameworks such as ReAct, AutoGPT, and memory-augmented approaches. Our contributions are threefold: (1) we situate SCL within the taxonomy of hybrid intelligence, differentiating it from prompt-centric and memory-only approaches; (2) we formally define Soft Symbolic Control and contrast it with neuro-symbolic AI; and (3) we derive three design principles for trustworthy agents: modular decomposition, adaptive symbolic governance, and transparent state management. We provide a complete open-source implementation demonstrating the R-CCAM loop architecture, alongside a live GPT-4o-powered travel planning agent. By connecting expert system principles with modern LLM capabilities, this work offers a practical and theoretically grounded path toward reliable, explainable, and governable AI agents.

翻译：大型语言模型智能体存在根本性的架构问题：推理与执行相互纠缠、记忆易失性以及动作序列不可控。我们提出结构化认知循环（SCL），这是一种模块化架构，将智能体认知明确划分为五个阶段：检索、认知、控制、动作与记忆（R-CCAM）。软符号控制作为SCL中的专用治理层，在保持神经推理灵活性的同时，对概率推断施加符号约束，从而恢复经典符号系统的可解释性与可控性。通过在多步条件推理任务上的实证验证，我们证明SCL能够实现零策略违规、消除冗余工具调用，并保持完整的决策可追溯性。这些结果弥补了ReAct、AutoGPT及记忆增强方法等现有框架的关键缺陷。我们的贡献包括三个方面：（1）将SCL置于混合智能的分类体系中，区分其与提示中心化及纯记忆化方法；（2）形式化定义软符号控制，并与神经符号AI进行对比；（3）推导出可信智能体的三项设计原则：模块化分解、自适应符号治理及透明状态管理。我们提供了完整的开源实现，展示R-CCAM循环架构，并附有一个基于GPT-4o的实时旅行规划智能体。通过将专家系统原理与现代LLM能力相结合，本工作为构建可靠、可解释且可治理的AI智能体提供了一条兼具实践性与理论基础的路径。