AI 中文总结
该研究针对LLM智能体复杂任务的个体智能局限，提出图工程范式，通过构建动态图结构组织协调异构智能体，为系统智能提供统一基础。
AI 中文摘要
大语言模型（LLMs）已从语言生成器演变为能完成复杂、长周期任务的自主智能体。这一演变催生了多种范式，包括用于激发模型能力的Prompt Engineering（提示工程）、用于管理信息访问的Context Engineering（上下文工程）、用于组织外部工具与资源的Harness Engineering（管控工程），以及用于支持持续反思与自我改进的Loop Engineering（循环工程）。然而，随着任务愈发复杂，个体智能面临根本性局限：许多任务需要异构专业知识、相互依赖的子任务、并行执行、独立验证及持久状态，超出了单一智能体的组织能力。增强单一智能体的能力或上下文无法解决这种架构不匹配问题；智能必须分布在多个专业智能体之间，并在系统层面进行组织。我们将此称为System Intelligence（系统智能）：智能体系统将多个智能组件组织协调为连贯、自适应整体以追求共同目标的能力。实现系统智能需要的不只是增加智能体，还需要明确的结构来组织工作、协调异构智能体、维护不断演变的执行状态。我们提出Graph Engineering（图工程）这一针对下一代智能体系统的新兴范式。与主要优化个体交互或智能体层面行为的现有范式不同，图工程构建明确、动态、不断演变的图结构，用于表示任务、智能体和系统状态。这些抽象为组织复杂目标、编排异构智能体、建模系统动态及实现可扩展的智能体进化提供了统一基础。我们系统综述了面向LLM智能体的图工程的原理、方法论及应用，相关论文、开源数据和项目已收集于此httpsURL。
英文摘要
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.
