As artificial intelligence (AI) systems evolve from stateless chatbots to autonomous multi-step agents, prompt engineering (PE), the discipline of crafting individual queries, proves necessary but insufficient. This paper introduces context engineering (CE) as a standalone discipline concerned with designing, structuring, and managing the entire informational environment in which an AI agent makes decisions. Drawing on vendor architectures (Google ADK, Anthropic, LangChain), current academic work (ACE framework, Google DeepMind's intelligent delegation), enterprise research (Deloitte, 2026; KPMG, 2026), and the author's experience building a multi-agent system, the paper proposes five context quality criteria: relevance, sufficiency, isolation, economy, and provenance, and frames context as the agent's operating system. Two higher-order disciplines follow. Intent engineering (IE) encodes organizational goals, values, and trade-off hierarchies into agent infrastructure. Specification engineering (SE) creates a machine-readable corpus of corporate policies and standards enabling autonomous operation of multi-agent systems at scale. Together these four disciplines form a cumulative pyramid maturity model of agent engineering, in which each level subsumes the previous one as a necessary foundation. Enterprise data reveals a gap: while 75% of enterprises plan agentic AI deployment within two years (Deloitte, 2026), deployment has surged and retreated as organizations confront scaling complexity (KPMG, 2026). The Klarna case illustrates a dual deficit, contextual and intentional. Whoever controls the agent's context controls its behavior; whoever controls its intent controls its strategy; whoever controls its specifications controls its scale.

翻译：随着人工智能系统从无状态的聊天机器人演变为自主的多步骤智能体，提示工程——即精心设计单个查询的学科——被证明是必要但不充分的。本文引入上下文工程作为一个独立的学科，它关注于设计、构建和管理智能体进行决策时所处的整个信息环境。借鉴供应商架构、当前学术工作、企业研究以及作者构建多智能体系统的经验，本文提出了五个上下文质量准则：相关性、充分性、隔离性、经济性和可溯源性，并将上下文框架定义为智能体的操作系统。由此衍生出两个更高阶的学科。意图工程将组织的目标、价值观和权衡层级编码到智能体基础设施中。规范工程则创建一个机器可读的企业政策和标准语料库，使得大规模多智能体系统的自主运行成为可能。这四个学科共同构成了一个累积的金字塔式智能体工程成熟度模型，其中每一层级都包含前一层级作为必要基础。企业数据显示出一个差距：虽然75%的企业计划在两年内部署智能体人工智能，但由于组织面临规模化复杂性，实际部署经历了激增与回落。Klarna案例说明了上下文和意图的双重缺失。谁控制了智能体的上下文，谁就控制了其行为；谁控制了其意图，谁就控制了其战略；谁控制了其规范，谁就控制了其规模。