Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents, which can leverage distinguished reasoning and language processing capabilities to takes a proactive, autonomous role to pursue users' goals. Nevertheless, there is a lack of systematic knowledge to guide practitioners in designing the agents considering challenges of goal-seeking (including generating instrumental goals and plans), such as hallucinations inherent in foundation models, explainability of reasoning process, complex accountability, etc. To address this issue, we have performed a systematic literature review to understand the state-of-the-art foundation model-based agents and the broader ecosystem. In this paper, we present a pattern catalogue consisting of 16 architectural patterns with analyses of the context, forces, and trade-offs as the outcomes from the previous literature review. The proposed catalogue can provide holistic guidance for the effective use of patterns, and support the architecture design of foundation model-based agents by facilitating goal-seeking and plan generation.

翻译：基础模型驱动的生成式人工智能促进了智能体的开发与部署，这类智能体能够利用卓越的推理与语言处理能力，以主动、自主的方式追求用户目标。然而，目前尚缺乏系统性知识来指导实践者设计智能体时应对目标导向挑战（包括生成工具性目标与规划），例如基础模型中固有的幻觉现象、推理过程的可解释性、复杂的问责机制等。为解决该问题，我们通过系统性文献综述，梳理了基于基础模型的最先进智能体及其生态系统现状。本文提出包含16种架构模式的模式目录，并在文献综述基础上分析各模式的上下文、约束条件与权衡因素。该目录能为模式的有效选用提供整体性指导，通过促进目标导向与规划生成，支撑基于基础模型的智能体架构设计。