As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstrate emergent abilities and broad expertise across natural language domains, their inherent unpredictability makes the implementation of LLM agents challenging, resulting in a gap between related research and the real-world implementation of such systems. To bridge this gap, this paper frames actionable insights and considerations from the research community in the context of established application paradigms to enable the construction and facilitate the informed deployment of robust LLM agents. Namely, we position relevant research findings into four broad categories--Planning, Memory, Tools, and Control Flow--based on common practices in application-focused literature and highlight practical considerations to make when designing agentic LLMs for real-world applications, such as handling stochasticity and managing resources efficiently. While we do not conduct empirical evaluations, we do provide the necessary background for discussing critical aspects of agentic LLM designs, both in academia and industry.

翻译：随着大型语言模型（LLM）能力的不断提升，将其作为自主智能体底层模型的兴趣日益增长。尽管LLM在自然语言领域展现出涌现能力与广泛的专业知识，但其固有的不可预测性使得LLM智能体的实现面临挑战，导致相关研究与实际系统部署之间存在差距。为弥合这一差距，本文结合成熟的应用范式，梳理研究界提出的可操作见解与设计考量，以支持构建稳健的LLM智能体并促进其有效部署。具体而言，我们基于应用导向文献中的常见实践，将相关研究成果归纳为四大类别——规划、记忆、工具与控制流——并着重阐述了面向实际应用设计智能LLM时需考虑的关键问题，例如处理随机性和高效管理资源等。尽管未进行实证评估，本文为学术界与工业界讨论智能LLM设计的核心要素提供了必要的背景知识。