Internet Engineering Task Force                            Z. Liang, Ed.Internet-Draft                                                    E. CuiIntended status: Informational          China Telecom Research InstituteExpires: 12 April 2026                                          Y. Cheng                            University of Science and Technology Beijing                                                          9 October 2025          AgentDNS: A Root Domain Naming System for LLM Agents                        draft-liang-agentdns-00Abstract   This document describes AgentDNS, a root domain naming and service   discovery system designed for Large Language Model (LLM) agents.   Inspired by the traditional Domain Name System (DNS), AgentDNS   enables agents to autonomously discover, resolve, and securely invoke   third-party agent and tool services across different vendors.   AgentDNS introduces a unified namespace, semantic service discovery,   protocol-aware interoperability, and unified authentication and   billing.  The system aims to address interoperability, service   discovery, and trust management challenges in large-scale agent   collaboration ecosystems.Status of This Memo   This Internet-Draft is submitted in full conformance with the   provisions of BCP 78 and BCP 79.   Internet-Drafts are working documents of the Internet Engineering   Task Force (IETF).  Note that other groups may also distribute   working documents as Internet-Drafts.  The list of current Internet-   Drafts is at https://datatracker.ietf.org/drafts/current/.   Internet-Drafts are draft documents valid for a maximum of six months   and may be updated, replaced, or obsoleted by other documents at any   time.  It is inappropriate to use Internet-Drafts as reference   material or to cite them other than as "work in progress."   This Internet-Draft will expire on 12 April 2026.Copyright Notice   Copyright (c) 2025 IETF Trust and the persons identified as the   document authors.  All rights reserved.Liang, et al.             Expires 12 April 2026                 [Page 1]Internet-Draft              Abbreviated Title               October 2025   This document is subject to BCP 78 and the IETF Trust's Legal   Provisions Relating to IETF Documents (https://trustee.ietf.org/   license-info) in effect on the date of publication of this document.   Please review these documents carefully, as they describe your rights   and restrictions with respect to this document.  Code Components   extracted from this document must include Revised BSD License text as   described in Section 4.e of the Trust Legal Provisions and are   provided without warranty as described in the Revised BSD License.Table of Contents   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2   2.  Related Work  . . . . . . . . . . . . . . . . . . . . . . . .   5   3.  AgentDNS Architecture . . . . . . . . . . . . . . . . . . . .   7     3.1.  AgentDNS System Overview  . . . . . . . . . . . . . . . .   7     3.2.  Service Naming  . . . . . . . . . . . . . . . . . . . . .  10     3.3.  Service Discovery . . . . . . . . . . . . . . . . . . . .  10     3.4.  Service Resolution  . . . . . . . . . . . . . . . . . . .  11     3.5.  Unified Authentication and Billing  . . . . . . . . . . .  12   4.  AgentDNS Case Study . . . . . . . . . . . . . . . . . . . . .  13   5.  Future Opportunities  . . . . . . . . . . . . . . . . . . . .  15   6.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  16   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  16   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  16   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  16     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  16     9.2.  Informative References  . . . . . . . . . . . . . . . . .  17   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  211.  Introduction   The rapid evolution of LLM agents across industries has introduced   new challenges in enabling seamless multi-agent collaboration.   Existing protocols such as the Model Context Protocol (MCP)   [MCP-Spec] and Agent-to-Agent (A2A) protocol [A2A-Spec] have improved   agent-tool interoperability and communication.  However, these   efforts lack a standardized root naming and discovery infrastructure   to support autonomous cross-vendor interactions.  As a result,   collaborations between agents still demands significant manual   effort, preventing the realization of true autonomous cooperation.   The specific challenges are as follows:   *  *The Service Discovery Challenge:* LLM agents typically generate      an action plan, where each action may require calling external      tools or agent services [RFC6763].  However, currently, services      from different vendors are not standardized in naming or      management, which forces developers to manually maintain service      information for each tool or agent.  This lack of standardizationLiang, et al.             Expires 12 April 2026                 [Page 2]Internet-Draft              Abbreviated Title               October 2025      makes it impossible for LLM agents to autonomously discover other      agents or tool services, hindering seamless integration and      collaboration between agents across different platforms.   *  *The Interoperability Challenge:* Currently, different vendors'      agents or tool services support various interoperability or      communication protocols, with typical examples including MCP, A2A,      and ANP (Agent Network Protocol Project 2025) [ANP-Doc].  We      anticipate that more interoperability protocols will emerge in the      future.  However, LLM agents are unable to autonomously recognize      and adapt to these differences, meaning they still require manual      configuration and management.  This lack of flexibility in      handling diverse protocols limits seamless agent-to-agent and      agent-to-tool communication across platforms.   *  *The Authentication and Billing Challenge:* Cross-vendor      collaboration is further complicated by security and      authentication challenges.  Each service provider typically      requires proprietary API keys, necessitating manual configuration      of multiple authentication credentials for agents.  This adds      significant overhead and disrupts seamless integration.  In      addition, billing systems are fragmented across vendors, requiring      manual intervention for payment setup.  As a result, agents are      unable to autonomously discover and invoke new third-party paid      agents or tool services without manual configuration.   To address these challenges, this document introduces AgentDNS, the   first root domain naming and resolution system designed for LLM   agents.  Inspired by the Internet's DNS [RFC1035], AgentDNS   introduces a unified namespace (*agentdns://*), natural language-   based service discovery, and unified authentication and billing.  As   shown in Figure 1, AgentDNS is compatible with protocols such as MCP   and A2A, allowing them to coexist seamlessly.   <preamble>:Illustrates the interaction between AgentDNS, A2A protocol, MCP, LLMs, and             enterprise applications across different agent frameworks.Liang, et al.             Expires 12 April 2026                 [Page 3]Internet-Draft              Abbreviated Title               October 2025                +-------------------------------------------+                | | AgentDNS Root Server |                  |                | +----------------------+   AgentDNS DB    |                | |         LLM          |                  |                +-----------+-------------------------------+                                         |                         +---------------+--------------+                         |               |              |                         v               |              v              +--------------------+    A2A    +--------------------+              | Agents (Vendor A)  | <-------> | Agents (Vendor B)  |              | +----------------+ |     |     | +----------------+ |              | | Agent Framework| |     |     | | Agent Framework| |              | +----------------+ |     |     | +----------------+ |              | |       LLM      | |     |     | |       LLM      | |              +--------------------+     |     +--------------------+                      |                  |               |                     MCP                 |              MCP                      |                  |               |                      v                  |               v          +--------------------------+   |    +--------------------------+          | API & Enterprise Systems |   |    | API & Enterprise Systems |          +--------------------------+   |    +--------------------------+                                         |                       Organizational / Technological Boundary   Figure 1: AgentDNS system and its relationship with A2A and MCP                              Protocols   With AgentDNS, agents can autonomously discover services,   authenticate, and interoperate seamlessly across organizational   boundaries.  AgentDNS redefines the multi-agent collaboration   ecosystem through four key functions:   *  *Unified Namespace with Semantic Information:* AgentDNS introduces      a semantically rich naming scheme (e.g., agentdns://org/category/      name) for agents and tool services, decoupling service identifier      name from physical addresses such as URLs.  This also enables the      semantic embedding of agent capabilities into their identifier      name, facilitating more efficient classification and retrieval of      agent and tool services.Liang, et al.             Expires 12 April 2026                 [Page 4]Internet-Draft              Abbreviated Title               October 2025   *  *Natural Language-Driven Service Discovery:* Agents can interact      with the AgentDNS root service using natural language queries to      discover third-party agents or tool services.  They can obtain the      corresponding service identifier names and related metadata,      including physical addresses, capabilities, and communication      protocol, etc.  Agents can also dynamically request the AgentDNS      root service to resolve an identifier name and retrieve the latest      metadata as needed.   *  *Protocol-Aware Interoperability:* AgentDNS enables agents to      dynamically discover the supported interoperability or      communication protocols of third-party agents and tool services by      resolving their identifier names into metadata.  This metadata      includes not only network addresses and capabilities, but also the      specific protocols (e.g., MCP, A2A, ANP) each service supports.      Based on this information, agents can autonomously select and      adapt to the appropriate protocol for communication, eliminating      the need for manual configuration.   *  *Unified Authentication and Billing:* AgentDNS replaces fragmented      API keys with a single-sign-on mechanism.  Agents authenticate      once with the AgentDNS root server to obtain time-bound access      tokens, valid across all registered services.  For billing,      AgentDNS serves as a unified billing platform: users pre-fund      accounts, usage costs are tracked and deducted in real-time, and      payments are automatically settled across vendors.  This enables      transparent billing and autonomous access to paid services by      agents.2.  Related Work   *LLM Agents*      LLM agents have rapidly emerged as a pivotal research frontier in      artificial intelligence, driven by their transformative potential      to bridge human-AI collaboration and autonomous problem-solving.      In the industrial, several LLM agents have been launched, such as      Deep Research [Deep-Research], Manus [Manus], and Cursor      (Anysphere 2025) [Cursor], etc.  Unlike traditional AI systems      constrained by predefined rules, LLM agents leverage the general-      purpose reasoning, contextual understanding, and multi-task      capabilities of LLMs to dynamically adapt to complex environments.      LLM agents have demonstrated broad application prospects across      various fields.  The future of LLM agents is expected to trend      towards multi-agent collaboration.  Researchers are increasingly      interested in how to design efficient communication protocols and      coordination mechanisms [hou2025model] [marro2024scalable] that      enable seamless cooperation among agents.  This collaborative      approach is seen as a key direction for advancing the capabilitiesLiang, et al.             Expires 12 April 2026                 [Page 5]Internet-Draft              Abbreviated Title               October 2025      and applications of LLM agents in the coming years.   *Agent Interaction Protocols*      *Model Context Protocol.* The Model Context Protocol (MCP) (Hou et      al.  2025) is an open standard developed by Anthropic, designed to      facilitate seamless interactions between LLM models and external      tools, data sources, and services.  Inspired by the concept of a      universal adapter, MCP aims to simplify the integration process,      much like how a USB-C port allows various devices to connect      effortlessly.  MCP operates on a client-server architecture.  The      AI application (such as a chatbot or an integrated development      environment) acts as the host and runs an MCP client, while each      external integration runs as an MCP server.  The server exposes      capabilities such as functions, data resources, or predefined      prompts, and the client connects to it to utilize these      capabilities.  This design allows AI models to interact with      external systems without directly accessing APIs, thereby      enhancing security and reducing the complexity of custom      integrations.      *Agent-to-Agent Protocol.* The Agent-to-Agent (A2A) protocol      (Google 2025) is introduced by Google, aimed at enabling seamless      communication and collaboration between LLM agents, regardless of      their underlying frameworks or vendors.  A2A was developed in      collaboration with over 50 technology partners, including major      companies like Atlassian, Salesforce, SAP, and MongoDB.  This      protocol uses HTTP-based APIs and JSON data format, ensuring      compatibility and ease of integration with existing enterprise IT      systems.  It supports various communication patterns, including      request-response, event-based communication, and streaming data      exchange.  A2A complements protocols like MCP, which focuses on      providing tools and context for agents.  A2A focuses on agent-to-      agent communication, allowing them to work together more      effectively.   *Domain Naming System*      The Domain Name System (DNS) [danzig1992analysis]      [cheshire2013rfc6763] serves as the critical naming and discovery      infrastructure for the human internet, translating memorable      domain names (e.g., example.  com) into physical addresses (IP      addresses) through its hierarchical, decentralized architecture.      While DNS effectively decouples human-readable names from machine-      level addressing, its design proves inadequate for the emerging      agent Internet where LLM agents require autonomous service      discovery and interoperability.  Traditional DNS lacks three      critical capabilities essential for agent ecosystems: service      discovery through natural language, querying service metadata      beyond physical addresses (including capabilities, protocols,Liang, et al.             Expires 12 April 2026                 [Page 6]Internet-Draft              Abbreviated Title               October 2025      etc.), and unified authentication and billing.  These limitations      necessitate AgentDNS-a purpose-built system that preserves DNS's      core benefits of naming and resolution while introducing agent-      specific innovations.3.  AgentDNS Architecture3.1.  AgentDNS System Overview   AgentDNS is a root service system for agent service naming,   discovery, and resolution, enabling seamless service discovery,   cross-vendor interoperability, unified authentication and billing.   As shown in Figure 2, the AgentDNS root server is the central hub of   the entire system, which connects agent users (e.g., Agent A) with   service providers (e.g., Agent Service B, Tool Service D).  The   AgentDNS root server comprises components including service   registration, service proxy pool[RFC5625], service management,   service search, service resolution, billing, authentication, etc.   The core components are as follows:   *  Service Registration Component: Agent or tool service vendors      register their services through this component.  The process      begins with organization registration, where developers first      create an organization account.  Under the organization's domain,      they then setup a service category and name to generate a globally      unique service identifier name (e.g., agentdns://org/category/      name).  Concurrently, developers must submit service metadata to      bind with the identifier, including network addresses (e.g.,      endpoints, URLs), supported interoperability protocols (e.g., MCP,      A2A), detailed service capabilities, etc.  This metadata is      securely stored in the AgentDNS database.  During subsequent      service discovery or resolution phases, the system performs      semantic matching by analyzing the identifier's category and the      metadata.  This ensures precise retrieval of services aligned with      an agent's operational requirements [RFC7720].   *  Service Proxy Pool: After a vendor registers a service, AgentDNS      creates a corresponding service proxy within the Service Proxy      Pool.  This proxy acts as an intermediary, forwarding service      invocation requests from user agents to the vendor's actual      service endpoint.  In other words, the user agent sends a service      request to the AgentDNS root server, which then routes the request      to the appropriate vendor for execution.  This design allows user      agents to authenticate only once with AgentDNS, eliminating the      need for separate registration and authentication with each      individual vendor.Liang, et al.             Expires 12 April 2026                 [Page 7]Internet-Draft              Abbreviated Title               October 2025   *  Service Search Component: User agents can send natural language      queries to the AgentDNS root server to discover relevant third-      party agents or tool services.  This component interprets the      query and performs intelligent retrieval using a combination of      keyword matching and retrieval-augmented generation (RAG)      [gao2023retrieval] techniques.  Based on the search results, it      returns a list of top-k candidate services that match the agent's      intent.  Each result includes the service identifier name,      physical endpoint, supported communication protocols, capability      descriptions, and pricing information.  The user agent can then      evaluate these candidates and choose the most appropriate one for      execution.  Once selected, the agent can directly invoke the      service by using the appropriate protocol and access the physical      endpoint with an authentication token issued by AgentDNS.   *  Service Resolution Component: User agents can cache service      identifier names and, during subsequent invocations, dynamically      request the AgentDNS root server to resolve these identifiers and      get the latest metadata as needed.   *  Service Management Component: This component handles the lifecycle      of these service proxies, including their creation, updates, and      deletion, ensuring that the proxy infrastructure remains up-to-      date and synchronized with the underlying services.   *  Service Billing Component: This component is responsible for      tracking and processing service invocation costs.  Users only need      to settle payments directly with AgentDNS, which then handles the      backend settlement with individual vendors.  This design      significantly simplifies the billing process for users by      eliminating the need for managing multiple vendor-specific payment      systems, enabling a streamlined and unified billing experience.   *  Authentication Component: This component handles identity      verification and access control for both user agents and service      providers.  Instead of requiring agents to authenticate separately      with each vendor, AgentDNS offers a unified authentication      mechanism.  User agents authenticate once with the AgentDNS root      server and receive a time-bound access token.  This token can be      used to securely access any registered third-party service without      additional logins.  By centralizing authentication, this component      ensures secure, efficient, and scalable access across a      heterogeneous agent ecosystem, while also reducing the operational      burden on both users and service vendors.Liang, et al.             Expires 12 April 2026                 [Page 8]Internet-Draft              Abbreviated Title               October 2025      <preamble>:                  This figure illustrates the architecture of the AgentDNS system, its API interface,                  and interactions with agents and services.                                               +-----------------------------------------+                           |          AgentDNS Root Server           |                           |  +----------------+   +--------------+  |                           |  |   Resolution   |   |    Search    |  |                           |  +----------------+   +--------------+  |                           |  |    Billing     |   |    Manage    |  |                           |  +----------------+   +--------------+  |                           |  | Authentication |   | Registration |  |                           |  +----------------+   +--------------+  |                           |          Service Proxy Pool             |                           |     (Agent Svc B, C, Tool Svc D...)     |                           |                                         |                           |        +------------------------+       |                           |        | AgentDNS API Server    |       |                           |        +------------------------+       |                           +---------------------+-------------------+                                        ▲          ▲         +--------------+               |          |         |   Agent A    |---------------+          |         +--------------+   Natural Lang Query     |               |                                   |               |                                   |               |                       +----------------------+               |                       | Service Registration |               |                       +----------------------+               |                                   ▲               |                                   |               |    Service Call                   |               | (Proxy by AgentDns)    +---------------------------+               +----------------------> | AgentSvcB, AgentSvcC, ... |                                        | Tool Service D ...        |                                        +---------------------------+                   Figure 2: AgentDNS System Architecture   Together, these components form the backbone of AgentDNS, providing a   unified framework that supports natural language-driven discovery,   protocol-aware interoperability, trustless authentication, and   unified billing-paving the way for truly autonomous multi-agent   ecosystems.  Next, we will provide a detailed introduction to   AgentDNS's service naming, service discovery, service resolution, and   unified authentication and billing mechanisms.Liang, et al.             Expires 12 April 2026                 [Page 9]Internet-Draft              Abbreviated Title               October 20253.2.  Service Naming   The AgentDNS service naming system provides a structured and globally   unique service identifier name for each registered agent or tool   service.  The identifier name follows the format as shown in   Figure 3.  The organization represents the name of the registering   entity, such as a company, university, or research lab.  Each   organization must go through a registration and verification process   to ensure uniqueness and authenticity.  The category denotes the   functional domain or classification of the agent service.  This can   be chosen manually by the developer or automatically generated by   AgentDNS, and it supports hierarchical structures—allowing for multi-   level categories using slashes (e.g., academic/nlp/summarization).   Finally, the name is the unique identifier for the specific agent   within the organization and category.  This name must be explicitly   defined by the developer.  Together, this structured naming   convention ensures precise identification, facilitates organized   discovery, and supports scalable service management within the   AgentDNS ecosystem.      <preamble>:                  This figure shows the structure of an AgentDNS service name, including prefix,                  organization, category, and agent name.                         Service Identifier Format:     agentdns://{organization}/{category}/{name}         |            |             |         |       Prefix    Organization    Category   Name     Example:     agentdns://example/academic/paperagent                     Figure 3: AgentDNS Service Naming3.3.  Service Discovery   The service discovery process is illustrated in Figure 4.  In step 1,   Agent A initiates a natural language query to the AgentDNS root   server, describing the desired service.  In the example, Agent A is   looking for an intelligent Agent capable of analyzing academic   papers.  In step 2, upon receiving the request, AgentDNS searches   through its registry of available services to identify those with the   required capabilities.  It returns a list of service identifiers   along with corresponding metadata, such as the proxy's physical   address, supported protocols, pricing information, and more.  This   discovery process employs a hybrid retrieval mechanism that combinesLiang, et al.             Expires 12 April 2026                [Page 10]Internet-Draft              Abbreviated Title               October 2025   keyword matching and RAG.  Specifically, we construct a knowledge   base using the capability descriptions of registered services.   During service discovery, hybrid retrieval is performed over these   capability descriptions to identify candidates that best match the   user agent's intent.  In step 3, after receiving the service   information, Agent A uses the appropriate protocol and an   authentication token issued by AgentDNS to directly access the   physical proxy address and initiate a service call.  Finally, in step   4, the AgentDNS proxy forwards the request to the actual service   endpoint hosted by the vendor, ensuring seamless interaction between   Agent A and the service provider.   <preamble>:Illustrates how Agent A discovers and interacts with an academic paper analysis               agent through the AgentDNS Root Server and proxy layers.            +------------------+                              +------------------------+  |     Agent A      |                              |  AgentDNS Root Server  |  +------------------+                              +------------------------+        |   ① Prompt: "I need an agent                         ▲        |      for academic paper analysis."                   |        |----------------------------------------------------->|        |                                                      |        |   ② Search Result:                                   |        |      agentdns://example/academic/paperagent          |        |<-----------------------------------------------------|        |                                                      |        |                                                      v        |   ③ Service call                        +------------------------+        |---------------------------------------> |   paperagent proxy     |                                                  +------------------------+                                                            |                                                            | ④ Forward                                                            v                                                  +------------------------+                                                  |      paperagent        |                                                  +------------------------+            Figure 4: AgentDNS Service Discovery Workflow3.4.  Service Resolution   As previously mentioned, user agents can cache service identifier   names and request the AgentDNS root server for updated metadata when   needed.  This functionality helps reduce the frequency of accessing   AgentDNS, improving response times and lowering operational costs.   The service resolution process is illustrated in Figure 5.  In step   1, agent vendors update the metadata associated with their agent   services.  In step 2, Agent A sends a resolution request to theLiang, et al.             Expires 12 April 2026                [Page 11]Internet-Draft              Abbreviated Title               October 2025   AgentDNS root server, providing the cached service identifier name to   retrieve the latest information.  In step 3, AgentDNS locates the   most recent metadata based on the identifier and returns it to Agent   A, ensuring that the service invocation uses up-to-date information.   <preamble>:               Illustrates how an agent (paperagent) updates its metadata to the AgentDNS Root Server,               and how another agent (Agent A) resolves the service via AgentDNS to retrieve updated               metadata.                                +-------------+                           +------------------------+            | paperagent  |                           | AgentDNS Root Server   |            +-------------+                           +------------------------+                  |                                            ▲            ① Metadata Update:                                 |              Old metadata -> New metadata                     |                  |------------------------------------------->|                                                               |                                                               |                                                      ② Service Resolution:                                              agentdns://example/academic/paperagent                                                               |                  |<-------------------------------------------|                  v            +-------------+            |  Agent A    |            +-------------+                  |            ③ New metadata                  |                  v            (uses updated metadata)                Figure 5: AgentDNS service resolution3.5.  Unified Authentication and Billing   AgentDNS introduces a unified authentication and billing mechanism by   acting as a proxy layer between user agents and third-party services.   As shown in Figure 6, when a user agent (e.g., Agent A) authenticates   once with the AgentDNS root server using its own access key (Key A),   it gains the ability to seamlessly invoke multiple external agent or   tool services without needing to manage individual credentials for   each provider.  Internally, the AgentDNS root server maintains a   service proxy pool that forwards user requests to the corresponding   third-party services.  For each thirdparty service, the proxy uses   the appropriate authentication key (e.g., Key B, C, or D), whichLiang, et al.             Expires 12 April 2026                [Page 12]Internet-Draft              Abbreviated Title               October 2025   corresponds to the access control requirements of the service   provider.  This abstraction decouples the user agent from vendor-   specific authentication logic.  Moreover, billing is centralized:   user agents are charged by AgentDNS based on their usage, while   AgentDNS handles settlements with the respective thirdparty services.   This model simplifies cross-vendor interoperability, enforces secure   access, and enables consistent billing across a heterogeneous service   ecosystem.   <preamble>:Agent A resolves services via the AgentDNS Root Server, including billing,               authentication, and service redirection using keys.            +--------+        +---------------------------+        +------------------+  | Agent  |        |   AgentDNS Root Server    |        | Service B        |  |   A    |------->| +-----------------------+ |------->| (via Key B)      |  |        |  Key A | |  Service Billing      | |        +------------------+  +--------+        | +-----------------------+ |                    | +-----------------------+ |        +------------------+                    | | Authentication        | |------->| Service C        |                    | +-----------------------+ |        | (via Key C)      |                    | +-----------------------+ |        +------------------+                    | | Agent Service B       | |                    | +-----------------------+ |        +------------------+                    | +-----------------------+ |------->| Tool Service D   |                    | | Agent Service C       | |        | (via Key D)      |                    | +-----------------------+ |        +------------------+                    | +-----------------------+ |                    | | Tool Service D        | |                    | +-----------------------+ |                    | (Via Service Proxy Pool)  |                    +---------------------------+        Figure 6: AgentDNS Unified Authentication and Billing4.  AgentDNS Case Study   In this section, we present a case study illustrating the interaction   between an agent and the AgentDNS root server.  The case demonstrates   the complete agent workflow—from generating an action plan   [huang2024understanding], to querying the AgentDNS root server for   service discovery, and finally to executing the planned actions.   The full process is illustrated in Figure 7.  After receiving a user   request—such as “Help me research agent communication protocols and   write a survey report”—the agent first invokes a LLM to generate an   action plan.  As shown in Figure 7, the generated plan in this case   is structured in JSON format and consists of multiple steps.  Each   step includes a description of its purpose, whether it requires aLiang, et al.             Expires 12 April 2026                [Page 13]Internet-Draft              Abbreviated Title               October 2025   service, and a natural language description of the desired service   functionality.  These services correspond to third-party agent or   tool services.  For example, Step 1 requires a search service to   retrieve relevant keywords, while Step 3 calls for a standards   retrieval service to query documents from organizations like IEEE   (IEEE Standards Association 2025) [ieee2025] or ITU-T (International   Telecommunication Union 2025) [itut2025].   <preamble>:Illustrates how an LLM-generated action plan is executed through AgentDNS-based             service discovery and agent invocation.        +---------------------------------+       +------------------------------+       +------------------------------+| Action Plan                     |       |  Service Discovery           |       | Action Execution             || (Generated by LLM)              |       |  (via AgentDNS Search)       |       | (Step by step)               |+---------------------------------+       +------------------------------+       +------------------------------+| steps:                          |       | agentdns://example/search/   |       | Execute step 1               || 1. Use search engines...        |       | searchagent                  |       | (Call searchagent)           ||    tool_required: true          |       |   - protocol: MCP            |       +------------------------------+|    tool_function: keyword search|       |   - cost: $1/million tokens  |       | Execute step 2               ||                                 |       |   - capability: keyword search|      | (LLM + Prompt)               || 2. Analyze communication...     |       +------------------------------+       +------------------------------+|    tool_required: false         |       | agentdns://example/standard/ |       | Execute step 3               ||                                 |       | standardagent                |       | (Call standardagent)         || 3. Summarize standardization    |       |   - protocol: MCP            |       +------------------------------+|    tool_required: true          |       |   - cost: free               |       | Execute step 4               ||    tool_function: query IEEE... |       |   - capability: standard query|      | (LLM + Prompt)               || 4. Write the research report    |       +------------------------------+       +------------------------------++---------------------------------+                    ^                                 ^        |                                              |                                 |        |----------------------------------------------|---------------------------------|                                        Uses AgentDNS Root Server                    Figure 7: AgentDNS Case Study   After generating the action plan, the agent submits a natural   language query to the AgentDNS root server to discover suitable   third-party services.  For instance, in Step 1, the agent sends the   tool function description directly to AgentDNS, which uses   intelligent retrieval methods to identify matching services.  Suppose   AgentDNS returns a service named agentdns://example/search/   searchagent; it also provides metadata such as the physical endpoint,   supported protocols, service cost, capabilities, and available APIs.   The agent uses this information to invoke the selected third-party   service.   Following service discovery, the agent enters the action execution   phase.  During this stage, it executes the steps of the action plan   in sequence.  When a step requires a service, the agent uses theLiang, et al.             Expires 12 April 2026                [Page 14]Internet-Draft              Abbreviated Title               October 2025   corresponding protocol to access the thirdparty service obtained from   AgentDNS and passes the result to the next step.  For steps that do   not involve external services, the agent inputs the step purpose   description, along with previous outputs and prompt instructions,   into the LLM for generation.  This process continues until all steps   in the action plan are completed.   This case study presents a simplified example, while in practice, the   structure and format of an action plan can be adapted to suit   different needs.  Importantly, the third-party service descriptions   within the action plan are expressed in natural language, which means   they are not tightly coupled with specific service identifiers, tool   names, or endpoint URLs.  AgentDNS plays a critical role in   decoupling the foundational agent model from vendor-specific details   such as service names, tool identifiers, and physical addresses,   enabling more flexible and scalable agent architectures.5.  Future Opportunities   While AgentDNS addresses fundamental challenges in service discovery,   interoperability, and billing in the agent ecosystem, numerous   directions remain open for future exploration.  These include   decentralized and federated architecture, AgentDNS-compatible agent   planning LLMs, privacy-preserving [RFC8932] and trusted discovery, as   well as AgentDNS service discovery optimization.  First, while the   current design of AgentDNS adopts a centralized architecture, future   iterations may benefit from decentralized or federated   [huang2024aggregate] architecture, such as blockchain   [karaarslan2018blockchain].  This would improve robustness, reduce   the risk of single points of failure, and enhance trust in cross-   organizational collaborations.  Second, training and fine-tuning   agent planning LLMs [wang2023describe] [hu2024agentgen] specifically   compatible with AgentDNS is also an important direction.  This can   involve constructing agent planning datasets and fine-tuning LLMs to   enhance their compatibility with AgentDNS.  Alternatively,   reinforcement learning techniques [wen2024reinforcing]   [jin2025search] [qi2024webrl] [peiyuan2024agile] can be used to train   agents to autonomously explore and optimize action sequences,   dynamically selecting and combining various services registered in   AgentDNS to maximize task success rates and efficiency.  Third,   security and privacy will remain central in crossvendor agent   collaboration.  Future directions may involve privacy-preserving   search and resolution, using technologies such as homomorphic   encryption [buban2025encrypted], differential privacy, and secure   multi-party computation.  AgentDNS could also integrate trust and   reputation systems to allow agents to evaluate service quality and   security risks before invocation.  Finally, the optimization of   AgentDNS service discovery and retrieval remains a critical area forLiang, et al.             Expires 12 April 2026                [Page 15]Internet-Draft              Abbreviated Title               October 2025   improving system performance and user experience.6.  Conclusion   The rapid advancement of LLM agents has exposed critical gaps in   cross-vendor service discovery, interoperability, and authentication,   hindering the vision of autonomous multiagent collaboration.  This   document introduces AgentDNS, a unified root domain naming system   designed to bridge these gaps by providing a semantically rich   namespace, natural language-driven service discovery, protocol-aware   interoperability, and trustless authentication and billing.  By   decoupling agent identifiers from physical addresses and embedding   dynamic metadata resolution, AgentDNS enables agents to autonomously   discover, resolve, and securely invoke services across organizational   and technological boundaries.  Our architecture and case studies   demonstrate its potential to streamline multi-agent workflows, reduce   manual overhead, and foster an open ecosystem for agent   collaboration.  Future works include decentralized and federated   architecture, AgentDNS-compatible agent planning LLMs, privacy-   preserving and trusted discovery, as well as AgentDNS service   discovery optimization, etc.7.  IANA Considerations   This memo includes no request to IANA.8.  Security Considerations   This document should not affect the security of the Internet.9.  References9.1.  Normative References   [RFC1035]  Mockapetris, P., "Domain names - implementation and              specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,              November 1987, <https://www.rfc-editor.org/info/rfc1035>.   [RFC6763]  Cheshire, S. and M. Krochmal, "DNS-Based Service              Discovery", RFC 6763, DOI 10.17487/RFC6763, February 2013,              <https://www.rfc-editor.org/info/rfc6763>.   [RFC5625]  Bellis, R., "DNS Proxy Implementation Guidelines",              BCP 152, RFC 5625, DOI 10.17487/RFC5625, August 2009,              <https://www.rfc-editor.org/info/rfc5625>.Liang, et al.             Expires 12 April 2026                [Page 16]Internet-Draft              Abbreviated Title               October 2025   [RFC7720]  Blanchet, M. and L. Liman, "DNS Root Name Service Protocol              and Deployment Requirements", BCP 40, RFC 7720,              DOI 10.17487/RFC7720, December 2015,              <https://www.rfc-editor.org/info/rfc7720>.   [RFC8932]  Dickinson, S., Overeinder, B., van Rijswijk-Deij, R., and              A. Mankin, "Recommendations for DNS Privacy Service              Operators", BCP 232, RFC 8932, DOI 10.17487/RFC8932,              October 2020, <https://www.rfc-editor.org/info/rfc8932>.9.2.  Informative References   [MCP-Spec] Model Context Protocol Specification Authors, "Model              Context Protocol (MCP) Specification", March 2025,              <https://modelcontextprotocol.io/              specification/2025-03-26>.   [A2A-Spec] Agent2Agent Protocol Specification Authors, "Agent2Agent              (A2A) Protocol Specification", 2025,              <https://github.com/google/A2A>.   [ANP-Doc]  Agent Network Protocol Authors, "Agent Network Protocol              (ANP) | Complete Guide to Agent Network Protocol", 2025,              <https://agentnetworkprotocol.com/en/docs/>.   [Deep-Research]              OpenAI, "Introducing Deep Learning Research", 2025,              <https://openai.com/index/introducing-deep-research/>.   [Manus]    Monica, "Manus: A New Kind of AI", 2025,              <https://manus.im/>.   [Cursor]   Cursor, "Cursor: A New Kind of AI Editor", 2025,              <https://cursor.com/en/agents>.   [hou2025model]              Hou, Xinyi., Zhao, Yanjie., Wang, Shenao., and Haoyu.              Wang, "Model context protocol (mcp): Landscape, security              threats, and future research directions", arXiv preprint              arXiv:2503.23278 2025, 2025.   [ding2024large]              Ding, Han., Li, Yinheng., Wang, Junhao., and Hang. Chen,              "Large language model agent in financial trading: A              survey", arXiv preprint arXiv:2408.06361 2024, 2024.Liang, et al.             Expires 12 April 2026                [Page 17]Internet-Draft              Abbreviated Title               October 2025   [chu2025llm]              Chu, Zhendong., Wang, Shen., Xie, Jian., Zhu, Tinghui.,              Yan, Yibo., Ye, Jinheng., Zhong, Aoxiao., Hu, Xuming.,              Liang, Jing., Yu, Philip S., and others, "Llm agents for              education: Advances and applications", arXiv preprint              arXiv:2503.11733 2025, 2025.   [alzubi2025open]              Alzubi, Salaheddin., Brooks, Creston., Chiniya, Purva.,              Contente, Edoardo., von Gerlach, Chiara., Irwin, Lucas.,              Jiang, Yihan., Kaz, Arda., Nguyen, Windsor., Oh, Sewoong.,              and others, "Open deep search: Democratizing search with              open-source reasoning agents", arXiv preprint              arXiv:2503.20201 2025, 2025.   [chen2024mindsearch]              Chen, Zehui., Liu, Kuikun., Wang, Qiuchen., Liu,              Jiangning., Zhang, Wenwei., Chen, Kai., and Feng. Zhao,              "Mindsearch: Mimicking human minds elicits deep ai              searcher", arXiv preprint arXiv:2407.20183 2024, 2024.   [luo2025large]              Luo, Junyu., Zhang, Weizhi., Yuan, Ye., Zhao, Yusheng.,              Yang, Junwei., Gu, Yiyang., Wu, Bohan., Chen, Binqi.,              Qiao, Ziyue., Long, Qingqing., and others, "Large Language              Model Agent: A Survey on Methodology, Applications and              Challenges", arXiv preprint arXiv:2503.21460 2025, 2025.   [googlea2a]              Google, "An open protocol enabling communication and              interoperability between opaque agentic applications.",              2025.   [marketsandmarkets_ai_agents]              {MarketsandMarkets}, "AI Agents Market", 2025.   [guo2024large]              Guo, Taicheng., Chen, Xiuying., Wang, Yaqi., Chang,              Ruidi., Pei, Shichao., Chawla, Nitesh V., Wiest, Olaf.,              and Xiangliang. Zhang, "Large language model based multi-              agents: A survey of progress and challenges", arXiv              preprint arXiv:2402.01680 2024, 2024.   [han2024llm]              Han, Shanshan., Zhang, Qifan., Yao, Yuhang., Jin,              Weizhao., Xu, Zhaozhuo., and Chaoyang. He, "LLM multi-              agent systems: Challenges and open problems", arXiv              preprint arXiv:2402.03578 2024, 2024.Liang, et al.             Expires 12 April 2026                [Page 18]Internet-Draft              Abbreviated Title               October 2025   [yang2025survey]              Yang, Yingxuan., Chai, Huacan., Song, Yuanyi., Qi,              Siyuan., Wen, Muning., Li, Ning., Liao, Junwei., Hu,              Haoyi., Lin, Jianghao., Chang, Gaowei., and others, "A              Survey of AI Agent Protocols", arXiv preprint              arXiv:2504.16736 2025, 2025.   [yan2025beyond]              Yan, Bingyu., Zhang, Xiaoming., Zhang, Litian., Zhang,              Lian., Zhou, Ziyi., Miao, Dezhuang., and Chaozhuo. Li,              "Beyond self-talk: A communication-centric survey of llm-              based multi-agent systems", arXiv preprint              arXiv:2502.14321 2025, 2025.   [cheshire2013rfc6763]              Cheshire, Stuart. and Marc. Krochmal, "RFC 6763: DNS-Based              Service Discovery", 2013.   [danzig1992analysis]              Danzig, Peter B., Obraczka, Katia., and Anant. Kumar, "An              analysis of wide-area name server traffic: A study of the              internet domain name system", Conference proceedings on              Communications architectures \& protocols 1992, 1992.   [openai2025deepresearch]              {OpenAI}, "Introducing Deep Research", 2025.   [manus2025]              {Monica}, "Leave it to Manus", 2025.   [cursor2025]              }, {Anysphere., "Cursor: The AI Code Editor", 2025.   [li2024improving]              Li, Yunxuan., Du, Yibing., Zhang, Jiageng., Hou, Le.,              Grabowski, Peter., Li, Yeqing., and Eugene. Ie, "Improving              multi-agent debate with sparse communication topology",              arXiv preprint arXiv:2406.11776 2024, 2024.   [marro2024scalable]              Marro, Samuele., La Malfa, Emanuele., Wright, Jesse., Li,              Guohao., Shadbolt, Nigel., Wooldridge, Michael., and              Philip. Torr, "A scalable communication protocol for              networks of large language models", arXiv preprint              arXiv:2410.11905 2024, 2024.Liang, et al.             Expires 12 April 2026                [Page 19]Internet-Draft              Abbreviated Title               October 2025   [huang2024understanding]              Huang, Xu., Liu, Weiwen., Chen, Xiaolong., Wang, Xingmei.,              Wang, Hao., Lian, Defu., Wang, Yasheng., Tang, Ruiming.,              and Enhong. Chen, "Understanding the planning of LLM              agents: A survey", arXiv preprint arXiv:2402.02716 2024,              2024.   [gao2023retrieval]              Gao, Yunfan., Xiong, Yun., Gao, Xinyu., Jia, Kangxiang.,              Pan, Jinliu., Bi, Yuxi., Dai, Yixin., Sun, Jiawei., Wang,              Haofen., and Haofen. Wang, "Retrieval-augmented generation              for large language models: A survey", arXiv preprint              arXiv:2312.10997 2023, 2023.   [ieee2025] Association, IEEE Standards., "IEEE Standards Association              Official Website", 2025.   [itut2025] Union, International Telecommunication., "ITU-T:              Telecommunication Standardization Sector", 2025.   [li2021b]  Li, Zecheng., Gao, Shang., Peng, Zhe., Guo, Songtao.,              Yang, Yuanyuan., and Bin. Xiao, "B-DNS: A secure and              efficient DNS based on the blockchain technology", IEEE              Transactions on Network Science and Engineering 2021,              2021.   [karaarslan2018blockchain]              Karaarslan, Enis. and Eylul. Adiguzel, "Blockchain based              DNS and PKI solutions", IEEE Communications Standards              Magazine 2018, 2018.   [huang2024aggregate]              Huang, Chih-Kai. and Guillaume. Pierre, "Aggregate              Monitoring for Geo-Distributed Kubernetes Cluster              Federations", IEEE Transactions on Cloud Computing 2024,              2024.   [wang2023describe]              Wang, Zihao., Cai, Shaofei., Chen, Guanzhou., Liu, Anji.,              Ma, Xiaojian Shawn., and Yitao. Liang, "Describe, explain,              plan and select: interactive planning with llms enables              open-world multi-task agents", Advances in Neural              Information Processing Systems 2023, 2023.Liang, et al.             Expires 12 April 2026                [Page 20]Internet-Draft              Abbreviated Title               October 2025   [hu2024agentgen]              Hu, Mengkang., Zhao, Pu., Xu, Can., Sun, Qingfeng., Lou,              Jianguang., Lin, Qingwei., Luo, Ping., and Saravan.              Rajmohan, "Agentgen: Enhancing planning abilities for              large language model based agent via environment and task              generation", arXiv preprint arXiv:2408.00764 2024, 2024.   [wen2024reinforcing]              Wen, Muning., Wan, Ziyu., Wang, Jun., Zhang, Weinan., and              Ying. Wen, "Reinforcing LLM Agents via Policy Optimization              with Action Decomposition", The Thirty-eighth Annual              Conference on Neural Information Processing Systems 2024,              2024.   [jin2025search]              Jin, Bowen., Zeng, Hansi., Yue, Zhenrui., Yoon, Jinsung.,              Arik, Sercan., Wang, Dong., Zamani, Hamed., and Jiawei.              Han, "Search-r1: Training llms to reason and leverage              search engines with reinforcement learning", arXiv              preprint arXiv:2503.09516 2025, 2025.   [qi2024webrl]              Qi, Zehan., Liu, Xiao., Iong, Iat Long., Lai, Hanyu., Sun,              Xueqiao., Zhao, Wenyi., Yang, Yu., Yang, Xinyue., Sun,              Jiadai., Yao, Shuntian., and others, "WebRL: Training LLM              Web Agents via Self-Evolving Online Curriculum              Reinforcement Learning", arXiv preprint              arXiv:2411.02337 2024, 2024.   [peiyuan2024agile]              Peiyuan, Feng., He, Yichen., Huang, Guanhua., Lin, Yuan.,              Zhang, Hanchong., Zhang, Yuchen., and Hang. Li, "AGILE: A              Novel Reinforcement Learning Framework of LLM Agents",              Advances in Neural Information Processing Systems 2024,              2024.   [buban2025encrypted]              Buban, James., Zhang, Hongyang., Angione, Claudio., Yang,              Harry., Farhan, Ahmad., Sultanov, Seyfal., Du, Michael.,              Ma, Xuran., Wang, Zihao., Zhao, Yue., and others,              "Encrypted Large Model Inference: The Equivariant              Encryption Paradigm", arXiv preprint              arXiv:2502.01013 2025, 2025.Authors' Addresses   Zhiyuan Liang (editor)   China Telecom Research InstituteLiang, et al.             Expires 12 April 2026                [Page 21]Internet-Draft              Abbreviated Title               October 2025   Email: liangzy17@chinatelecom.cn   Enfang Cui   China Telecom Research Institute   Email: cuief@chinatelecom.cn   Yujun Cheng   University of Science and Technology Beijing   Email: yjcheng@tsinghua.edu.cnLiang, et al.             Expires 12 April 2026                [Page 22]