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Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives

Source: arXiv cs.AI By Haibo Jin, Suijin Wang, Xucheng Yu, Haojing Luo
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Anthropic's Claude researchers propose a new framework for LLM tool use, focusing on reusable tool primitives. This approach aims to enhance harness engineering, making it easier for agents to integrate and utilize external tools effectively. The system allows for the creation of modular, agent-native components that can be composed to build more sophisticated tool-using capabilities. This research could lead to more robust and versatile AI agents capable of performing complex tasks by leveraging a wider array of specialized tools.

Why it matters

Why it matters: This research introduces a novel method for improving how AI models, like Anthropic's Claude, interact with external tools. By creating reusable 'primitives,' the system simplifies the development of complex tool-use workflows. This contrasts with current methods that often require bespoke integrations. It affects developers building AI agents and potentially end-users who will benefit from more capable AI assistants. Competitors like Google Gemini and OpenAI are also heavily invested in agentic capabilities and tool use, making this a key area for advancement. Future work will likely focus on scaling these primitives and demonstrating their effectiveness across diverse tasks.

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Where the other five stand

Related: OpenAI: First impressions of GPT-6 Astra from developers · Google: Pairing Google Antigravity with Gemini 3.7 Flash solves notable multi-agent math and engineering problems. · Microsoft: Build an AI agent without leaving VS Code · Meta: An Organizational Second Brain: Building an AI That Learns From Experts · xAI: Ajeya Cotra – "This might be the clearest warning shot we ever get"

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Source
arXiv cs.AI (arxiv.org)
Author
Haibo Jin, Suijin Wang, Xucheng Yu, Haojing Luo
Company
Anthropic · Web · Research
Products
Claude
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Questions people ask

What is the main goal of this research?
The research aims to improve how large language models (LLMs) use external tools by creating reusable, agent-native tool primitives. This enhances 'harness engineering' for more effective tool integration.
How does this approach benefit AI agents?
It allows agents to more easily integrate and utilize a variety of specialized tools. This modular approach can lead to more sophisticated and versatile AI agents capable of complex tasks.

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Page generated Sep 3, 2026. Summaries are Subvolts' own; the story belongs to arXiv cs.AI.