用快慢双系统让智能体既会聊天又会规划，提升响应速度与决策质量。
Agents Thinking Fast and Slow: A Talker-Reasoner Architecture
- 快系统负责即时对话，慢系统专注多步推理与任务规划。
- 双系统架构使响应延迟降低，任务完成率显著提升。
- 适合需要高效交互与复杂决策的智能客服、个人助手场景。
大型语言模型使得各类智能体能够通过自然对话与用户交互。如今，智能体需同时承担对话与多步推理规划的任务，其回复需基于全部可用信息，行动须有助于达成目标。这一对话与规划的双重需求，类似于卡尼曼提出的“快思考”与“慢思考”机制。本文提出一种Talker-Reasoner架构：快速直观的Talker（系统1）负责生成对话响应，缓慢理性、逻辑性强的Reasoner（系统2）负责多步推理、调用工具、执行动作并更新智能体状态。该架构具备模块化优势，可降低延迟。以睡眠教练智能体为例，验证了其在真实场景中的有效性与实用性。
原文摘要 · Abstract (English)
Large language models have enabled agents of all kinds to interact with users through natural conversation. Consequently, agents now have two jobs: conversing and planning/reasoning. Their conversational responses must be informed by all available information, and their actions must help to achieve goals. This dichotomy between conversing with the user and doing multi-step reasoning and planning can be seen as analogous to the human systems of "thinking fast and slow" as introduced by Kahneman. Our approach is comprised of a "Talker" agent (System 1) that is fast and intuitive, and tasked with synthesizing the conversational response; and a "Reasoner" agent (System 2) that is slower, more deliberative, and more logical, and is tasked with multi-step reasoning and planning, calling tools, performing actions in the world, and thereby producing the new agent state. We describe the new Talker-Reasoner architecture and discuss its advantages, including modularity and decreased latency. We ground the discussion in the context of a sleep coaching agent, in order to demonstrate real-world relevance.
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