The emergence of large language model (LLM)-based agent frameworks has shifted the primary challenge in building domain-expert AI agents from raw capability to effective encoding of domain expertise. Two dominant paradigms -- code-first development, which embeds expertise in deterministic pipelines, and prompt-first development, which captures expertise in static system prompts -- both treat agent construction as a discrete engineering phase preceding deployment. We argue that this sequential assumption creates a fundamental mismatch with the nature of domain expertise, which is substantially tacit, deeply personal, and continuously evolving. We propose Nurture-First Development (NFD), a paradigm in which agents are initialized with minimal scaffolding and progressively grown through structured conversational interaction with domain practitioners. The central mechanism is the Knowledge Crystallization Cycle, whereby fragmented knowledge embedded in operational dialogue is periodically consolidated into structured, reusable knowledge assets. We formalize NFD through: (1) a Three-Layer Cognitive Architecture organizing agent knowledge by volatility and personalization degree; (2) the Knowledge Crystallization Cycle with formal definitions of crystallization operations and efficiency metrics; and (3) an operational framework comprising a Dual-Workspace Pattern and Spiral Development Model. We illustrate the paradigm through a detailed case study on building a financial research agent for U.S. equity analysis and discuss the conditions, limitations, and broader implications of NFD for human-agent co-evolution.

翻译：基于大语言模型（LLM）的智能体框架的出现，使得构建领域专家AI智能体的核心挑战从原始能力转向了领域专业知识的有效编码。当前两种主流范式——将专业知识嵌入确定性流程的代码优先开发，以及将专业知识捕获于静态系统提示的提示优先开发——均将智能体构建视为部署前的一个离散工程阶段。我们认为，这种顺序性假设与领域专业知识的本质存在根本性错配，因为后者在很大程度上是隐性的、高度个人化的且持续演进的。我们提出培育优先开发范式，该范式下智能体以最小化脚手架初始化，并通过与领域从业者的结构化对话交互逐步成长。其核心机制是知识结晶循环，即嵌入操作对话中的碎片化知识被定期整合为结构化、可重用的知识资产。我们通过以下方面形式化NFD：（1）按知识易变性和个性化程度组织智能体知识的三层认知架构；（2）包含结晶操作形式化定义及效率度量的知识结晶循环；（3）由双工作区模式与螺旋开发模型构成的操作框架。我们通过一个为美国股票分析构建金融研究智能体的详细案例研究阐释该范式，并讨论了NFD的适用条件、局限性及其对人机协同进化的更广泛意义。