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🚀Shippingscore 104.2May 18, 2026·2605.18747cs.CLcs.AI

Code as Agent Harness

Xuying Ning, Katherine Tieu, Dongqi Fu, Tianxin Wei, Zihao Li, Yuanchen Bei, Jiaru Zou, Mengting Ai, Zhining Liu, Ting-Wei Li, Lingjie Chen, Yanjun Zhao, Ke Yang, Bingxuan Li, Cheng Qian, Gaotang Li, Xiao Lin, Zhichen Zeng, Ruizhong Qiu, Sirui Chen, Yifan Sun, Xiyuan Yang, Ruida Wang, Rui Pan, Chenyuan Yang, Dylan Zhang, Liri Fang, Zikun Cui, Yang Cao, Pan Chen, Dorothy Sun, Ren Chen, Mahesh Srinivasan, Nipun Mathur, Yinglong Xia, Hong Li, Hong Yan, Pan Lu, Lingming Zhang, Tong Zhang, Hanghang Tong, Jingrui He

PDF ↗arXiv ↗
Narrative

A survey arguing that code has evolved from an LLM output target into the structural backbone of agentic systems — the "harness" through which agents reason, act, store memory, use tools, and verify their own outputs. It organizes this framing across three layers: how code interfaces with agent capabilities, how mechanisms like planning and tool use make execution reliable, and how code artifacts coordinate multi-agent workflows. No quantitative benchmarks or ablations are offered; this is a conceptual taxonomy and literature organization, not an empirical contribution.

No production traction yet. The only substantive downstream repo is a 17-star "awesome list" companion maintained by one of the paper's authors, which is essentially a bibliography extension of the survey itself. Zero citations on Semantic Scholar. The framing is coherent and the application domains covered (GUI automation, DevOps, embodied agents, scientific discovery) are genuinely active areas, but this paper is early-stage literature organization rather than something builders can act on directly.

Abstract

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.

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