This survey paper examines the recent advancements in AI agent
implementations, with a focus on their ability to achieve complex goals that
require enhanced reasoning, planning, and tool execution capabilities. The
primary objectives of this work are to a) communicate the current capabilities
and limitations of existing AI agent implementations, b) share insights gained
from our observations of these systems in action, and c) suggest important
considerations for future developments in AI agent design. We achieve this by
providing overviews of single-agent and multi-agent architectures, identifying
key patterns and divergences in design choices, and evaluating their overall
impact on accomplishing a provided goal. Our contribution outlines key themes
when selecting an agentic architecture, the impact of leadership on agent
systems, agent communication styles, and key phases for planning, execution,
and reflection that enable robust AI agent systems.

这篇综述性论文研究了 AI 代理实现的最新进展，重点关注它们在实现需要增强推理、规划和工具执行能力的复杂目标方面的能力。该研究的主要目标是：a) 传达现有 AI 代理实现的当前能力和局限性，b) 分享我们在实际运行这些系统中获得的见解，c) 提出 AI 代理设计未来发展的重要考虑因素。我们通过提供单一代理和多代理架构的概述，确定设计选择中的关键模式和差异，并评估它们对完成特定目标的总体影响，实现了这一目标。我们的贡献概述了在选择代理架构时的关键主题，领导对代理系统的影响，代理的沟通风格，以及规划、执行和反思的关键阶段，从而实现强大的 AI 代理系统。