With the development of foundation model (FM), agentic AI systems are getting more attention, yet their inherent issues like hallucination and poor reasoning, coupled with the frequent ad-hoc nature of system design, lead to unreliable and brittle applications. Existing efforts to characterise agentic design patterns often lack a rigorous systems-theoretic foundation, resulting in high-level or convenience-based taxonomies that are difficult to implement. This paper addresses this gap by introducing a principled methodology for engineering robust AI agents. We propose two primary contributions: first, a novel system-theoretic framework that deconstructs an agentic AI system into five core, interacting functional subsystems: Reasoning & World Model, Perception & Grounding, Action Execution, Learning & Adaptation, and Inter-Agent Communication. Second, derived from this architecture and directly mapped to a comprehensive taxonomy of agentic challenges, we present a collection of 12 agentic design patterns. These patterns - categorised as Foundational, Cognitive & Decisional, Execution & Interaction, and Adaptive & Learning - offer reusable, structural solutions to recurring problems in agent design. The utility of the framework is demonstrated by a case study on the ReAct framework, showing how the proposed patterns can rectify systemic architectural deficiencies. This work provides a foundational language and a structured methodology to standardise agentic design communication among researchers and engineers, leading to more modular, understandable, and reliable autonomous systems.

翻译：随着基础模型（FM）的发展，智能体AI系统正受到越来越多的关注，但其固有的幻觉和推理能力差等问题，加之系统设计常具有临时性，导致应用不可靠且脆弱。现有刻画智能体设计模式的努力往往缺乏严格的系统理论基础，导致难以实施的高层次或基于便利性的分类法。本文通过引入一种工程化鲁棒AI智能体的原则性方法来解决这一空白。我们提出了两项主要贡献：首先，一个新颖的系统理论框架，将智能体AI系统解构为五个核心的、相互作用的功​​能子系统：推理与世界模型、感知与接地、动作执行、学习与适应，以及智能体间通信。其次，基于此架构并直接映射到智能体挑战的全面分类法，我们提出了一套包含12个智能体设计模式的集合。这些模式——分为基础类、认知与决策类、执行与交互类以及适应与学习类——为智能体设计中反复出现的问题提供了可重用的结构性解决方案。通过对ReAct框架的案例研究，展示了该框架的实用性，说明了所提出的模式如何能够纠正系统性的架构缺陷。这项工作为研究人员和工程师之间标准化智能体设计交流提供了一种基础语言和结构化方法，从而导向更模块化、更易理解且更可靠的自适应系统。