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). At the core of SCL is Soft Symbolic Control, an adaptive governance mechanism that applies symbolic constraints to probabilistic inference, preserving neural flexibility while 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：推理与执行纠缠、记忆易失性以及不可控的动作序列。本文提出结构化认知循环（SCL），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、行动与记忆（R-CCAM）。SCL的核心是软符号控制机制——一种自适应治理机制，通过符号约束作用于概率推理，在保持神经模型灵活性的同时，恢复了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实现了零策略违规、消除了冗余工具调用，并保持了完整的决策可追溯性。这些结果弥补了现有框架（如ReAct、AutoGPT及记忆增强方法）的关键缺陷。我们的贡献包括三个方面：（1）将SCL置于混合智能分类体系中，区分其与提示中心化及纯记忆方法的差异；（2）形式化定义软符号控制，并与神经符号AI进行对比；（3）提出可信智能体的三项设计原则：模块化分解、自适应符号治理与透明状态管理。我们提供了完整的开源实现，展示了R-CCAM循环架构，并部署了基于GPT-4o的实时旅行规划智能体。通过连接专家系统原理与现代LLM能力，本研究为构建可靠、可解释且可治理的AI智能体提供了兼具实践价值与理论基础的路径。