Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating the staffing models AI was expected to transform. Yet the meta-skills that make these tools effective, prompt engineering (interaction-level optimization) and context engineering (structured input pipeline design), are domain-portable: a practitioner who masters them can apply them to any purpose-built AI tool in any domain. This paper defines Augment Engineering as the discipline of orchestrating multiple purpose-built AI tools across distinct professional domains, applying prompt and context engineering as portable competencies that transfer across tool boundaries. We present a six-phase orchestration methodology and four portability metrics. A 5-month formative case study (November 2025 to March 2026) documents a single practitioner applying these skills across a ten-component orchestration stack spanning seven professional domains, producing work products that would traditionally involve separate domain specialists. Two quantitative observations are consistent with the framework's predictions: a Cochran-Armitage trend test (n = 200 interactions across two chat LLMs, p < 0.01) shows first-pass acceptance rising with prompt-sophistication level, and a Wright's Law fit (n = 82 artifacts, p < 0.01) shows production acceleration across the artifact portfolio. Because all observations come from a single practitioner, the inferential statistics are exploratory and hypothesis-generating rather than confirmatory; portability across the full portfolio awaits multi-practitioner replication. Augment Engineering completes a three-discipline progression: Prompt Engineering (one tool), Context Engineering (reproducible pipelines), Augment Engineering (a portfolio of tools across domains).

翻译：组织在各专业领域部署了越来越多的独立专用AI工具，通常为每个工具聘用领域专家，这又回到了AI本应变革的人员配置模式。然而，使这些工具有效的元技能——提示工程（交互层面优化）和上下文工程（结构化输入流水线设计）——具有领域可迁移性：掌握这些技能的实践者可将它们应用于任何领域的任何专用AI工具。本文定义“增广工程”为跨不同专业领域编排多个专用AI工具的学科，将提示工程和上下文工程作为跨工具边界可迁移的可移植能力。我们提出六阶段编排方法论和四项可移植性指标。一项为期5个月的形成性案例研究（2025年11月至2026年3月）记录了单一实践者将这些技能应用于跨越七个专业领域的十组件编排堆栈，产出的工作产品传统上需由不同领域专家完成。两个定量观察结果与框架预测一致：Cochran-Armitage趋势检验（n=200次跨两个聊天LLM的交互，p<0.01）显示首次通过接受率随提示复杂度水平上升；Wright定律拟合（n=82个工件，p<0.01）显示工件组合的生产加速。由于所有观察均来自单一实践者，推论统计为探索性假设生成而非验证性；完整组合的可迁移性有待多实践者复现。增广工程完成了三门学科的演进：提示工程（单一工具）、上下文工程（可复现流水线）、增广工程（跨领域工具组合）。