Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry their own brand and audit trail. What separates them from the general-purpose tier is fit, not capability: each is built for one job, by the engineer who will maintain it. No published practice sets out how to build one end to end. The pieces are everywhere (function-calling APIs, the Model Context Protocol, code agents to pair with), but the practice that chains them lives in podcasts, blogs, and leaked system prompts. This paper writes that practice down as a methodology, Agents All the Way Down: two preconditions crossed once and kept, then three practices repeated for the agent's life. The preconditions are (P1) Substrate, the LLM as a software component, framed as tools, then system, then messages under prompt-caching; and (P2) Building blocks: function calling, MCP, CLI orchestration, the liteshell pattern, the agent loop, skills, characters, hooks, and scaffolding. The practices are (P3) prototype with a general-purpose agent; (P4) harvest, fold, and ship the result as a CLI, the Turtle pattern; and (P5) agent-tests-agent, in which a general-purpose agent drives it through behavioural scenarios, a complement to classical testing, not a replacement. The working loop is P3 to P4 to P5 and back, and one corollary falls out for free: multi-agent orchestration is just CLI composition. The methodology is framework-free by construction. It was distilled from the AAC, a custom agent for the open-source LAMB platform, built in about ten days by one developer with an AI pair-programmer and in production . We present it as a transferable practice, independent of any language or framework.

翻译：定制化AI智能体是驻留在自有应用程序中的智能体，它们与自有数据和工具交互，执行自定安全边界，并承载自有品牌与审计轨迹。使其区别于通用层级的关键在于适配性而非能力：每个智能体均由负责维护的工程师针对特定任务构建。现有文献尚未系统阐述如何端到端构建此类智能体。相关技术组件虽已广泛存在（如函数调用API、模型上下文协议、配合使用的代码智能体），但整合这些组件的实践方法却散见于播客、博客及泄露的系统提示中。本文将此实践经验总结为"层层递进之智能体"方法论：设置两个一次性建立后持续遵循的前提条件，以及三个在智能体生命周期中循环实施的实践。前提条件包括：(P1) 底层架构——将大语言模型作为软件组件，通过工具化、系统化及基于提示缓存的报文机制进行架构；(P2) 构建模块——函数调用、MCP、CLI编排、轻量化Shell模式、智能体循环、技能集、角色设定、钩子机制与脚手架。实践包括：(P3) 基于通用智能体原型验证；(P4) 收割、整合并封装为CLI的"海龟模式"；(P5) 智能体互测模式——由通用智能体驱动被测试智能体完成行为场景验证，作为经典测试方法的补充而非替代。工作循环由P3至P4至P5再返回起点，并自然衍生出推论：多智能体编排本质上就是CLI组合。该方法论通过设计实现无框架依赖，其提炼自AAC——面向开源LAMB平台构建的定制化智能体，由单开发者配合AI结对编程在约十天内完成开发并部署至生产环境。本文将其定位为独立于任何语言或框架的可迁移实践方案。