📐 Harness 工程 · 中文翻译

参考文献(保留原文)

  • Jimenez et al. [2024] C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan. SWE-bench: Can language models resolve real-world GitHub issues? In Proc. ICLR, 2024.
  • Yang et al. [2024] J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. Narasimhan, and O. Press. SWE-agent: Agent-computer interfaces enable automated software engineering. In Proc. NeurIPS, 2024.
  • Wang et al. [2024a] X. Wang, Y. Chen, L. Yuan, Y. Zhang, Y. Li, H. Peng, and H. Ji. Executable code actions elicit better LLM agents. In Proc. ICML, 2024.
  • Yao et al. [2023] S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao. ReAct: Synergizing reasoning and acting in language models. In Proc. ICLR, 2023.
  • Weng [2023] L. Weng. LLM-powered autonomous agents. Lil’Log, 2023. https://lilianweng.github.io/posts/2023-06-23-agent/.
  • Wang et al. [2024b] L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, et al. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 2024.
  • Hong et al. [2024] S. Hong, M. Zhuge, J. Chen, X. Zheng, Y. Cheng, C. Zhang, J. Wang, Z. Wang, S. K. S. Yau, Z. Lin, et al. MetaGPT: Meta programming for a multi-agent collaborative framework. In Proc. ICLR, 2024.
  • Wu et al. [2023] Q. Wu, G. Banber, Y. Zhang, Y. Wu, B. Li, Z. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang. AutoGen: Enabling next-gen LLM applications via multi-agent conversation. arXiv preprint arXiv:2308.08155, 2023.
  • Fourney et al. [2024] A. Fourney, G. Bansal, H. Mozannar, C. Tan, E. Horvitz, et al. Magentic-One: A generalist multi-agent system for solving complex tasks. arXiv preprint arXiv:2411.04468, 2024.
  • Schick et al. [2023] T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom. Toolformer: Language models can teach themselves to use tools. In Proc. NeurIPS, 2023.
  • Patil et al. [2024] S. G. Patil, T. Zhang, X. Wang, and J. E. Gonzalez. Gorilla: Large language model connected with massive APIs. In Proc. NeurIPS, 2024.
  • Model Context Protocol [2024] Anthropic. Model Context Protocol specification. https://modelcontextprotocol.io/, 2024.
  • OpenHands [2024] OpenHands Contributors. OpenHands: An open platform for AI software developers as generalist agents. https://github.com/OpenHands/OpenHands (formerly All-Hands-AI); agent architecture: https://github.com/OpenHands/software-agent-sdk, 2024.
  • Gauthier [2024] P. Gauthier. Aider: AI pair programming in your terminal. https://github.com/Aider-AI/aider, 2024.
  • Mini-SWE-Agent Contributors [2024] Mini-SWE-Agent Contributors. Mini-SWE-Agent: A minimal software engineering agent. https://github.com/mini-swe-agent/mini-swe-agent, 2024.
  • Schluntz & Zhang [2024] E. Schluntz and B. Zhang. Building effective agents. Anthropic Engineering Blog, December 2024. https://www.anthropic.com/research/building-effective-agents.
  • Hadfield et al. [2025] J. Hadfield, B. Zhang, K. Lien, F. Scholz, J. Fox, and D. Ford. How we built our multi-agent research system. Anthropic Engineering Blog, June 2025. https://www.anthropic.com/engineering/multi-agent-research-system.
  • Aizawa et al. [2025] K. Aizawa, B. Zhang, Z. Witten, and others. Writing effective tools for AI agents. Anthropic Engineering Blog, September 2025. https://www.anthropic.com/engineering/writing-tools-for-agents.
  • Rajasekaran et al. [2025] P. Rajasekaran, E. Dixon, C. Ryan, J. Hadfield, R. Ayub, H. Moran, C. Rueb, and C. Jennings. Effective context engineering for AI agents. Anthropic Engineering Blog, September 2025. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents.
  • Mistral AI [2026] Mistral AI. Mistral Vibe: A coding agent built on Mistral models. https://github.com/mistralai/mistral-vibe (formerly mistralai/vibe), 2026. Version 2.19.1, accessed July 2026.
  • Google [2026] Google. Gemini CLI: An open-source AI agent that brings the power of Gemini to the terminal. https://github.com/google-gemini/gemini-cli, 2026. Version 0.50.0, accessed July 2026; consumer service retired June 2026 in the announced transition to the closed-source Antigravity CLI.
  • Nous Research [2026] Nous Research. Hermes Agent: The agent that grows with you. https://github.com/NousResearch/hermes-agent, 2026. Version v2026.7.7.2 (0.18.2), accessed July 2026.
  • Zechner [2026] M. Zechner. Pi: The coding-agent harness you can make your own. https://github.com/earendil-works/pi, 2026. Version 0.80.6, accessed July 2026.
  • Anomaly [2026] Anomaly (formerly SST). OpenCode: The AI coding agent built for the terminal. https://github.com/anomalyco/opencode (formerly sst/opencode), 2026. Version 1.17.18, accessed July 2026.
  • Databricks [2026] Databricks. Introducing Omnigent: A meta-harness to combine, control and share your agents. https://github.com/omnigent-ai/omnigent, June 2026. Version 0.4.0, accessed July 2026.
  • xAI [2026] xAI. Grok Build: xAI’s coding agent CLI. https://x.ai/cli, May 2026. Accessed July 2026.
  • Claw Code Contributors [2026] Claw Code Contributors. Claw Code: A clean-room, provider-agnostic reimplementation of the Claude Code architecture. https://claw-code.codes/, 2026. Accessed July 2026.
  • Trivedy [2026] V. Trivedy. The anatomy of an agent harness. LangChain Blog, March 2026. https://www.langchain.com/blog/the-anatomy-of-an-agent-harness.
  • The Deep Feed [2026] The Deep Feed. Agent harness engineering: The new discipline, decoded. https://www.thedeepfeed.ai/posts/2026-05-09-agent-harness-engineering-the-discipline/, May 2026.
  • Böckeler [2026] B. Böckeler. Harness engineering for coding agent users. martinfowler.com, April 2026. https://martinfowler.com/articles/harness-engineering.html.
  • Steinberger [2026] P. Steinberger. “You shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.” Post on X, June 2026.
  • Osmani [2026] A. Osmani. Loop engineering. https://addyosmani.com/blog/loop-engineering/, June 2026.
  • Macedo [2026] S. O. de Macedo. What makes a harness a harness: Necessary and sufficient conditions for an agent harness. arXiv preprint arXiv:2606.10106, June 2026.
  • Rombaut [2026] B. Rombaut. Inside the scaffold: A source-code taxonomy of coding agent architectures. arXiv preprint arXiv:2604.03515, April 2026.
  • HARBOR [2026] HARBOR Authors. HARBOR: Automated harness optimization. arXiv preprint arXiv:2604.20938, April 2026.
  • Code-as-Harness [2026] Code-as-Harness Authors. Code as agent harness. arXiv preprint arXiv:2605.18747, May 2026.
  • LangChain [2026] LangChain. Deep Agents: Overview. https://docs.langchain.com/oss/python/deepagents/overview, 2026. Accessed July 2026.
  • Anthropic [2026] Anthropic. Claude Agent SDK: Build production AI agents with Claude Code as a library. https://code.claude.com/docs/en/agent-sdk/overview, 2026. Accessed July 2026.
  • Pydantic [2026] Pydantic Services Inc. Pydantic AI Harness. https://github.com/pydantic/pydantic-ai-harness, 2026. Version 0.6.0, accessed July 2026.
  • Strands Agents [2026] Strands Agents. Harness SDK. https://github.com/strands-agents/harness-sdk, 2026. Accessed July 2026.
  • Agent Client Protocol [2025] Agent Client Protocol Working Group. Agent Client Protocol (ACP) specification. https://agentclientprotocol.com/, 2025.
  • Google [2025] Google. Agent-to-Agent (A2A) Protocol specification. https://a2a-protocol.org/, 2025.
  • Agent Skills [2025] Agent Skills Working Group. Agent Skills specification: file-system convention for capability bundles. https://agentskills.io/, 2025.
  • Zhang et al. [2024a] Y. Zhang, H. Ruan, Z. Fan, and A. Roychoudhury. AutoCodeRover: Autonomous program improvement. In Proc. ISSTA, 2024. https://arxiv.org/abs/2404.05427.
  • Wang et al. [2025a] X. Wang, B. Li, Y. Song, F. F. Xu, X. Tang, et al. OpenHands: An open platform for AI software developers as generalist agents. In Proc. ICLR, 2025. https://arxiv.org/abs/2407.16741.
  • Chen et al. [2024a] D. Chen, S. Lin, M. Zeng, D. Zan, et al. CodeR: Issue resolving with multi-agent and task graphs. arXiv preprint arXiv:2406.01304, 2024. https://arxiv.org/abs/2406.01304.
  • Tao et al. [2024] W. Tao, Y. Zhou, Y. Wang, W. Zhang, H. Zhang, and Y. Cheng. MAGIS: LLM-based multi-agent framework for GitHub issue resolution. In Proc. NeurIPS, 2024. https://arxiv.org/abs/2403.17927.
  • Arora et al. [2024] D. Arora, A. Sonwane, N. Wadhwa, A. Mehrotra, S. Utpala, R. Bairi, A. Kanade, and N. Natarajan. MASAI: Modular architecture for software-engineering AI agents. arXiv preprint arXiv:2406.11638, 2024. https://arxiv.org/abs/2406.11638.
  • Ma et al. [2024] Y. Ma, R. Cao, Y. Cao, Y. Zhang, J. Chen, Y. Liu, Y. Liu, B. Li, F. Huang, and Y. Li. Lingma SWE-GPT: An open development-process-centric language model for automated software improvement. arXiv preprint arXiv:2411.00622, 2024. https://arxiv.org/abs/2411.00622.
  • Qian et al. [2024] C. Qian, W. Liu, H. Liu, N. Chen, Y. Dang, et al. ChatDev: Communicative agents for software development. In Proc. ACL, 2024. https://arxiv.org/abs/2307.07924.
  • Xia et al. [2024] C. S. Xia, Y. Deng, S. Dunn, and L. Zhang. Agentless: Demystifying LLM-based software engineering agents. arXiv preprint arXiv:2407.01489, 2024. https://arxiv.org/abs/2407.01489.
  • Antoniades et al. [2025] A. Antoniades, A. Örwall, K. Zhang, Y. Xie, A. Goyal, and W. Wang. SWE-Search: Enhancing software agents with Monte Carlo tree search and iterative refinement. In Proc. ICLR, 2025. https://arxiv.org/abs/2410.20285.
  • Zhang et al. [2024b] K. Zhang, W. Yao, Z. Liu, Y. Feng, et al. Diversity empowers intelligence: Integrating expertise of software engineering agents. arXiv preprint arXiv:2408.07060, 2024. https://arxiv.org/abs/2408.07060.
  • Shinn et al. [2023] N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan, and S. Yao. Reflexion: Language agents with verbal reinforcement learning. In Proc. NeurIPS, 2023. https://arxiv.org/abs/2303.11366.
  • Madaan et al. [2023] A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, et al. Self-refine: Iterative refinement with self-feedback. In Proc. NeurIPS, 2023. https://arxiv.org/abs/2303.17651.
  • Gou et al. [2024] Z. Gou, Z. Shao, Y. Gong, Y. Shen, Y. Yang, N. Duan, and W. Chen. CRITIC: Large language models can self-correct with tool-interactive critiquing. In Proc. ICLR, 2024. https://arxiv.org/abs/2305.11738.
  • Chen et al. [2024b] X. Chen, M. Lin, N. Schärli, and D. Zhou. Teaching large language models to self-debug. In Proc. ICLR, 2024. https://arxiv.org/abs/2304.05128.
  • Qin et al. [2024] Y. Qin, S. Liang, Y. Ye, K. Zhu, et al. ToolLLM: Facilitating large language models to master 16000+ real-world APIs. In Proc. ICLR, 2024. https://arxiv.org/abs/2307.16789.
  • Du et al. [2024] Y. Du, F. Wei, and H. Zhang. AnyTool: Self-reflective, hierarchical agents for large-scale API calls. In Proc. ICML, 2024. https://arxiv.org/abs/2402.04253.
  • Zhang et al. [2023] F. Zhang, B. Chen, Y. Zhang, J. Keung, J. Liu, D. Zan, Y. Mao, J.-G. Lou, and W. Chen. RepoCoder: Repository-level code completion through iterative retrieval and generation. In Proc. EMNLP, 2023. https://arxiv.org/abs/2303.12570.
  • Liu et al. [2024a] T. Liu, C. Xu, and J. McAuley. RepoBench: Benchmarking repository-level code auto-completion systems. In Proc. ICLR, 2024. https://arxiv.org/abs/2306.03091.
  • Ding et al. [2023] Y. Ding, Z. Wang, W. U. Ahmad, H. Ding, M. Tan, et al. CrossCodeEval: A diverse and multilingual benchmark for cross-file code completion. In Proc. NeurIPS Datasets and Benchmarks, 2023. https://arxiv.org/abs/2310.11248.
  • Wang et al. [2025b] Z. Z. Wang, A. Asai, X. V. Yu, F. F. Xu, Y. Xie, G. Neubig, and D. Fried. CodeRAG-Bench: Can retrieval augment code generation? In Findings of NAACL, 2025. https://arxiv.org/abs/2406.14497.
  • Bogomolov et al. [2024] E. Bogomolov, A. Eliseeva, T. Galimzyanov, E. Glukhov, et al. Long Code Arena: A set of benchmarks for long-context code models. arXiv preprint arXiv:2406.11612, 2024. https://arxiv.org/abs/2406.11612.
  • Luo et al. [2024] Q. Luo, Y. Ye, S. Liang, Z. Zhang, Y. Qin, et al. RepoAgent: An LLM-powered open-source framework for repository-level code documentation generation. In Proc. EMNLP System Demonstrations, 2024. https://arxiv.org/abs/2402.16667.
  • Xi et al. [2023] Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, et al. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864, 2023. https://arxiv.org/abs/2309.07864.
  • Guo et al. [2024] T. Guo, X. Chen, Y. Wang, R. Chang, S. Pei, N. V. Chawla, O. Wiest, and X. Zhang. Large language model based multi-agents: A survey of progress and challenges. In Proc. IJCAI Survey Track, 2024. https://arxiv.org/abs/2402.01680.
  • Liu et al. [2025] J. Liu, K. Wang, Y. Chen, X. Peng, Z. Chen, L. Zhang, and Y. Lou. Large language model-based agents for software engineering: A survey. ACM Trans. Softw. Eng. Methodol., 2025. To appear. https://arxiv.org/abs/2409.02977.
  • Liu et al. [2024b] X. Liu, H. Yu, H. Zhang, Y. Xu, X. Lei, et al. AgentBench: Evaluating LLMs as agents. In Proc. ICLR, 2024. https://arxiv.org/abs/2308.03688.
  • Yao et al. [2023b] S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. In Proc. NeurIPS, 2023. https://arxiv.org/abs/2305.10601.
  • Wang et al. [2023] L. Wang, W. Xu, Y. Lan, Z. Hu, Y. Lan, R. K.-W. Lee, and E.-P. Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. In Proc. ACL, 2023. https://arxiv.org/abs/2305.04091.
  • Zhang et al. [2024c] Y. Zhang, R. Sun, Y. Chen, T. Pfister, R. Zhang, and S. Ö. Arik. Chain of agents: Large language models collaborating on long-context tasks. In Proc. NeurIPS, 2024. https://arxiv.org/abs/2406.02818.
  • Zhou et al. [2024] A. Zhou, K. Yan, M. Shlapentokh-Rothman, H. Wang, and Y.-X. Wang. Language agent tree search unifies reasoning, acting, and planning in language models. In Proc. ICML, 2024. https://arxiv.org/abs/2310.04406.
  • Packer et al. [2023] C. Packer, S. Wooders, K. Lin, V. Fang, S. G. Patil, I. Stoica, and J. E. Gonzalez. MemGPT: Towards LLMs as operating systems. arXiv preprint arXiv:2310.08560, 2023. https://arxiv.org/abs/2310.08560.
  • Jiang et al. [2023] H. Jiang, Q. Wu, C.-Y. Lin, Y. Yang, and L. Qiu. LLMLingua: Compressing prompts for accelerated inference of large language models. In Proc. EMNLP, 2023. https://arxiv.org/abs/2310.05736.
  • Debenedetti et al. [2024] E. Debenedetti, J. Zhang, M. Balunović, L. Beurer-Kellner, M. Fischer, and F. Tramèr. AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents. In Proc. NeurIPS Datasets and Benchmarks, 2024. https://arxiv.org/abs/2406.13352.
  • Greshake et al. [2023] K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz, and M. Fritz. Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. In Proc. AISec, 2023. https://arxiv.org/abs/2302.12173.
  • Yuan et al. [2024] T. Yuan, Z. He, L. Dong, Y. Wang, et al. R-Judge: Benchmarking safety risk awareness for LLM agents. In Findings of EMNLP, 2024. https://arxiv.org/abs/2401.10019.
  • Ye et al. [2024] J. Ye, S. Li, G. Li, C. Huang, S. Gao, Y. Wu, Q. Zhang, T. Gui, and X. Huang. ToolSword: Unveiling safety issues of large language models in tool learning across three stages. In Proc. ACL, 2024. https://arxiv.org/abs/2402.10753.
  • Mitra et al. [2024] A. Mitra, L. Del Corro, G. Zheng, S. Mahajan, D. Rouhana, et al. AgentInstruct: Toward generative teaching with agentic flows. arXiv preprint arXiv:2407.03502, 2024. https://arxiv.org/abs/2407.03502.
  • Gim et al. [2024] I. Gim, G. Chen, S.-s. Lee, N. Sarda, A. Khandelwal, and L. Zhong. Prompt cache: Modular attention reuse for low-latency inference. In Proc. MLSys, 2024. https://arxiv.org/abs/2311.04934.
  • Chen et al. [2024c] L. Chen, M. Zaharia, and J. Zou. FrugalGPT: How to use large language models while reducing cost and improving performance. Trans. Mach. Learn. Res., 2024. https://arxiv.org/abs/2305.05176.
  • Ong et al. [2025] I. Ong, A. Almahairi, V. Wu, W.-L. Chiang, T. Wu, J. E. Gonzalez, M. W. Kadous, and I. Stoica. RouteLLM: Learning to route LLMs with preference data. In Proc. ICLR, 2025. https://arxiv.org/abs/2406.18665.
  • Ding et al. [2024] D. Ding, A. Mallick, C. Wang, R. Sim, S. Mukherjee, V. Rühle, L. V. S. Lakshmanan, and A. H. Awadallah. Hybrid LLM: Cost-efficient and quality-aware query routing. In Proc. ICLR, 2024. https://arxiv.org/abs/2404.14618.
  • Lin et al. [2026] X. Lin, C. Ruan, B. Rozière, M. Tufano, M. Velez, and B. Shen. Agentic harness engineering: Observability-driven automatic evolution of coding-agent harnesses. arXiv preprint arXiv:2604.25850, April 2026.
  • Guo et al. [2025] H. Guo, Y. Hao, Y. Zhang, et al. A measurement study of Model Context Protocol ecosystem. arXiv preprint arXiv:2509.25292, 2025. https://arxiv.org/abs/2509.25292.
  • Xu and Yan [2026] R. Xu and Y. Yan. Agent skills for large language models: Architecture, acquisition, security, and the path forward. arXiv preprint arXiv:2602.12430, 2026. https://arxiv.org/abs/2602.12430.
  • Saha and Hemanth [2026] S. Saha and P. Hemanth. Skilldex: A package manager and registry for agent skill packages with hierarchical scope-based distribution. arXiv preprint arXiv:2604.16911, 2026. https://arxiv.org/abs/2604.16911.
  • Guo et al. [2026] Z. Guo, Z. Chen, et al. SkillProbe: Security auditing for emerging agent skill marketplaces via multi-agent collaboration. arXiv preprint arXiv:2603.21019, 2026. https://arxiv.org/abs/2603.21019.
  • Kapoor et al. [2025] S. Kapoor, N. Kolt, and S. Lazar. Resist platform-controlled AI agents and champion user-centric agent advocates. In Proc. ICML, 2025. https://arxiv.org/abs/2505.04345.
  • Robbes et al. [2026] R. Robbes, T. Matricon, et al. Agentic much? Adoption of coding agents on GitHub. arXiv preprint arXiv:2601.18341, 2026. https://arxiv.org/abs/2601.18341.
  • Li et al. [2025b] H. Li, H. Zhang, and A. E. Hassan. The rise of AI teammates in Software Engineering 3.0: How autonomous coding agents are reshaping software engineering. arXiv preprint arXiv:2507.15003, 2025. https://arxiv.org/abs/2507.15003.