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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.22368 |
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| _version_ | 1866910972810100736 |
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| author | Cui, Enfang Cheng, Yujun She, Rui Liu, Dan Liang, Zhiyuan Guo, Minxin Li, Tianzheng Wei, Qian Xing, Wenjuan Zhong, Zhijie |
| author_facet | Cui, Enfang Cheng, Yujun She, Rui Liu, Dan Liang, Zhiyuan Guo, Minxin Li, Tianzheng Wei, Qian Xing, Wenjuan Zhong, Zhijie |
| contents | The rapid evolution of Large Language Model (LLM) agents has highlighted critical challenges in cross-vendor service discovery, interoperability, and communication. Existing protocols like model context protocol and agent-to-agent protocol have made significant strides in standardizing interoperability between agents and tools, as well as communication among multi-agents. However, there remains a lack of standardized protocols and solutions for service discovery across different agent and tool vendors. In this paper, we propose AgentDNS, a root domain naming and service discovery system designed to enable LLM agents to autonomously discover, resolve, and securely invoke third-party agent and tool services across organizational and technological boundaries. Inspired by the principles of the traditional DNS, AgentDNS introduces a structured mechanism for service registration, semantic service discovery, secure invocation, and unified billing. We detail the architecture, core functionalities, and use cases of AgentDNS, demonstrating its potential to streamline multi-agent collaboration in real-world scenarios. The source code will be published on https://github.com/agentdns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AgentDNS: A Root Domain Naming System for LLM Agents Cui, Enfang Cheng, Yujun She, Rui Liu, Dan Liang, Zhiyuan Guo, Minxin Li, Tianzheng Wei, Qian Xing, Wenjuan Zhong, Zhijie Artificial Intelligence The rapid evolution of Large Language Model (LLM) agents has highlighted critical challenges in cross-vendor service discovery, interoperability, and communication. Existing protocols like model context protocol and agent-to-agent protocol have made significant strides in standardizing interoperability between agents and tools, as well as communication among multi-agents. However, there remains a lack of standardized protocols and solutions for service discovery across different agent and tool vendors. In this paper, we propose AgentDNS, a root domain naming and service discovery system designed to enable LLM agents to autonomously discover, resolve, and securely invoke third-party agent and tool services across organizational and technological boundaries. Inspired by the principles of the traditional DNS, AgentDNS introduces a structured mechanism for service registration, semantic service discovery, secure invocation, and unified billing. We detail the architecture, core functionalities, and use cases of AgentDNS, demonstrating its potential to streamline multi-agent collaboration in real-world scenarios. The source code will be published on https://github.com/agentdns. |
| title | AgentDNS: A Root Domain Naming System for LLM Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.22368 |