AI 单打独斗到头了，现在拼的是系统
大模型从会说话变成会干活，但一个人干不了大工程。这篇提出一个转折：与其把单个 AI 喂得更强，不如把多个专长不同的 AI 用一张动态的图组织起来——节点是任务、是 AI、是状态，边是协作关系，图会随着任务推进自己长。这叫 Graph Engineering，是继提示词、上下文、工具编排之后的第四代范式。它承认一个硬事实：复杂任务需要异构专家、并行执行、独立验证和持续状态，单个智能体再强也装不下。所以智能从个体身上挪到系统层面，靠结构取胜。这不是你明天能用的技巧，而是理解下一代 AI 系统为什么长成团队、而不是巨人的关键视角。
📄 原文摘要(英文)
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent's organizational capacity. Augmenting one agent's capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system level. We call this System Intelligence: an agent system's ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole pursuing a shared objective. Achieving it requires more than adding agents; it demands explicit structures to organize work, coordinate heterogeneous agents, and maintain evolving execution states. We introduce Graph Engineering, an emerging paradigm for next-generation agent systems. Unlike prior paradigms that mainly optimize individual interactions or agent-level behavior, Graph Engineering constructs explicit, dynamic, evolving graph structures representing tasks, agents, and system states. These abstractions provide a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution. We systematically review the principles, methodologies, and applications of Graph Engineering for LLM agents. Related papers, open-source data, and projects are collected at https://github.com/DEEP-JLU/Awesome-Graph-Engineering.