这篇综述把代码在智能体系统中的角色从“输出”提升到了“基础设施”层面，做智能体框架设计或工具链开发的团队值得一读，能帮你理清当前方法的脉络和未来方向。
本文提出“代码即智能体框架”概念，认为代码不仅是输出，更是智能体推理、行动、环境建模和执行验证的操作基础。论文从三个层次系统梳理：框架接口（代码连接推理与行动）、框架机制（规划、记忆、工具使用与反馈控制）、框架扩展（单智能体到多智能体协作）。覆盖编程助手、GUI/OS自动化、具身智能、科学发现、DevOps等应用场景，并指出评估、验证、状态共享等开放挑战。该视角为构建可执行、可验证、有状态的AI智能体系统提供了统一路线图。
Code as Agent Harness
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long-horizon execution, together with feedback-driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.