让多个智能体协作完成复杂任务，用图结构实现系统级智能。
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
- 用动态图结构组织多智能体的任务与状态
- 突破单个智能体局限，支持异构协作与持续演化
- 适合构建复杂、长期、需协同的AI系统
大语言模型已从语言生成器演变为能执行复杂长周期任务的自主智能体。现有范式如提示工程、上下文工程、工具工程和循环工程主要优化个体智能，但面对需要异构专长、依赖子任务、并行执行、独立验证和持久状态的复杂任务时，单一智能体的能力存在根本瓶颈。解决之道在于将智能分布到多个专用智能体，并在系统层面进行组织——即‘系统智能’：通过显式结构协调多智能体，形成适应性整体以达成共同目标。为此，我们提出‘图工程’这一新范式，构建显式、动态、演化的图结构来表示任务、智能体与系统状态，统一支撑复杂目标分解、异构智能体调度、系统动态建模与可扩展演化。本文系统梳理了图工程的原则、方法与应用，并收集相关论文、开源数据与项目至https://github.com/DEEP-JLU/Awesome-Graph-Engineering。
原文摘要 · Abstract (English)
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.
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