



Network Working Group                                            Y. Zhou
Internet-Draft                                 ANP Open Source Community
Intended status: Informational                                    K. Yao
Expires: 23 April 2026                                      China Mobile
                                                                   M. Yu
                                                           China Telecom
                                                                  M. Han
                                                            China Unicom
                                                                   C. Li
                                                                  Huawei
                                                         20 October 2025


                    Framework for AI Agent Networks
                draft-zyyhl-agent-networks-framework-01

Abstract

   This document defines the framework of AI agent networks based on
   Agent Network Protocol (ANP) protocol.  [ANP] It provides the basic
   functions that needs to support AI agent communication in the AI
   agent networks within the trust domain.

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 23 April 2026.

Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   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.



Zhou, et al.              Expires 23 April 2026                 [Page 1]

Internet-Draft          Agent Networks Framework            October 2025


   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  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Overview  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.2.  Scope . . . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.3.  Requirements Language . . . . . . . . . . . . . . . . . .   4
   2.  Terms and Definitions . . . . . . . . . . . . . . . . . . . .   4
   3.  Overview of the operation . . . . . . . . . . . . . . . . . .   4
     3.1.  Roles . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Protocol Flow . . . . . . . . . . . . . . . . . . . . . .   5
   4.  Digital Identifier  . . . . . . . . . . . . . . . . . . . . .   6
   5.  Agent Description . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Agent Description Document Format . . . . . . . . . . . .   7
       5.1.1.  Natural Language Format . . . . . . . . . . . . . . .   7
       5.1.2.  Structured Format . . . . . . . . . . . . . . . . . .   8
     5.2.  Agent Information Interaction Mechanism . . . . . . . . .   8
       5.2.1.  Information . . . . . . . . . . . . . . . . . . . . .   8
       5.2.2.  Interface . . . . . . . . . . . . . . . . . . . . . .   8
     5.3.  Security Mechanism  . . . . . . . . . . . . . . . . . . .   9
     5.4.  Integrity Verification  . . . . . . . . . . . . . . . . .   9
   6.  Agent Registration  . . . . . . . . . . . . . . . . . . . . .   9
     6.1.  Self-Declaration Mode . . . . . . . . . . . . . . . . . .   9
     6.2.  Centralized Registration Mode . . . . . . . . . . . . . .  10
   7.  Agent Discovery . . . . . . . . . . . . . . . . . . . . . . .  11
     7.1.  Proactive Discovery Mode (Corresponding to Self-Declaration
           Mode) . . . . . . . . . . . . . . . . . . . . . . . . . .  11
     7.2.  Centralized Query Mode (Corresponding to Centralized
           Registration Mode)  . . . . . . . . . . . . . . . . . . .  11
   8.  Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . .  12
     8.1.  Overview  . . . . . . . . . . . . . . . . . . . . . . . .  12
     8.2.  Task States . . . . . . . . . . . . . . . . . . . . . . .  13
     8.3.  Task coordination . . . . . . . . . . . . . . . . . . . .  13
   9.  Message mode  . . . . . . . . . . . . . . . . . . . . . . . .  14
     9.1.  Point-to-Point Communication  . . . . . . . . . . . . . .  14
     9.2.  Group Communication . . . . . . . . . . . . . . . . . . .  15
     9.3.  PUB/SUB Communication . . . . . . . . . . . . . . . . . .  15
   10. Multimodality . . . . . . . . . . . . . . . . . . . . . . . .  15
   11. Session management  . . . . . . . . . . . . . . . . . . . . .  15
     11.1.  Session Establishment and Control  . . . . . . . . . . .  16
     11.2.  Differentiated QoS Guarantees  . . . . . . . . . . . . .  16
   12. Routing . . . . . . . . . . . . . . . . . . . . . . . . . . .  16
     12.1.  Agent ID-based Route look-up . . . . . . . . . . . . . .  16



Zhou, et al.              Expires 23 April 2026                 [Page 2]

Internet-Draft          Agent Networks Framework            October 2025


     12.2.  Semantic-based Route resolution  . . . . . . . . . . . .  17
   13. Protocol Stack Considerations . . . . . . . . . . . . . . . .  17
   14. Security Considerations . . . . . . . . . . . . . . . . . . .  18
   15. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  19
   16. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .  19
   17. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  19
   18. References  . . . . . . . . . . . . . . . . . . . . . . . . .  19
     18.1.  Normative References . . . . . . . . . . . . . . . . . .  19
     18.2.  Informative References . . . . . . . . . . . . . . . . .  20
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  20

1.  Introduction

1.1.  Overview

   With the development of AI agent technology, its application
   scenarios have been continuously expanding.  From initial simple task
   execution to complex collaborative tasks among multiple agents,
   agents have demonstrated great potential in various fields.  This
   multi-agent collaboration model can fully leverage the strengths of
   individual agents, improving the quality and efficiency of task
   execution.  However, as the demand for multi-agent collaboration
   grows, defining standardized communication protocols among agents to
   achieve wide-area interconnection, cross-domain interoperability, and
   secure collaboration has become an urgent issue to address.

   To meet the communication needs of AI agents and promote the
   widespread services of multi-agent collaboration
   [I-D.stephan-ai-agent-6g], it is imperative to define standardized
   agent communication protocols that support interconnection,
   interoperability, and secure scalability between agents in trust
   domain.

   In this draft we propose to use Agent Network Protocol (ANP) as a
   baseline for further description.

1.2.  Scope

   From the perspective of network service domain division, future
   agents can be simply categorized into 3 types based on their
   deployment locations: terminal-side agents, network-side agents, and
   agents outside the network.  This draft mainly focuses on the
   communication between agents directly managed within the operator's
   network, i.e. the communication between the first two types of
   agents:

   *  Communication between different terminal-side agents registered in
      the same network service domain.



Zhou, et al.              Expires 23 April 2026                 [Page 3]

Internet-Draft          Agent Networks Framework            October 2025


   *  Communication between terminal-side agents and network-side agents
      registered in the same network service domain.

   *  Communication between network-side agents registered in the same
      network service domain.

   Furthermore, the communication between agents registered in different
   network domains is not within the scope of this draft.

1.3.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

2.  Terms and Definitions

   Task: Task is actions required to achieve a specific goal.  These
   actions can be physical or cognitive.

   Task chain: A Task chain defines an ordered set of tasks and ordering
   constraints that is to be applied to, e.g., packets, frames, or
   flows.  The implied order may not be a linear progression as the
   architecture allows for task chain of more than one branch, and also
   allows for cases where there is flexibility in the order in which
   tasks need to be applied.

   Coordinator Agent: An agent that receives tasks and decomposes or
   distributes tasks to other agents.

   Execution Agent: An agent responsible for executing tasks distributed
   by the Coordinator Agent.

3.  Overview of the operation

3.1.  Roles

   The Agent communication network defines three roles:

   *  AI Agent: An automated intelligent entity that achieves a specific
      goal (autonomously or not) on behalf of another entity, by e.g.
      interacting with its environment, acquiring contextual
      information, reasoning, self-learning, decision-making, and
      executing tasks (independently or in collaboration with other AI
      Agents).




Zhou, et al.              Expires 23 April 2026                 [Page 4]

Internet-Draft          Agent Networks Framework            October 2025


   *  Agent Registration Server: The server enables Agents to register
      their capabilities, and discover each other’s capabilities based
      on intent, task or other information.

   *  Agent Communication Server The server enables Agents to
      communicate and collaborate with each other, which provides
      session management and routing function.

3.2.  Protocol Flow

+------+                           +-------------+                            +------+
|      |                           |             |<-(A)-Capability Register---|      |
|      |                           |             |                            |      |
|      |                           |    Agent    |-(B)-------Response-------->|      |
|      |-(C)-Capability Discover-->| Registration|                            |      |
|      |                           |    Server   |                            |      |
|      |<-(D)----Matched Agents----|             |                            |      |
|  AI  |                           |             |                            |  AI  |
| Agent|                           +-------------+                            | Agent|
|   A  |                                                                      |   B  |
|      |                           +-------------+                            |      |
|      |-(E)Communication Request->|             |                            |      |
|      |                           |    Agent    |-(F)Communication Request-->|      |
|      |                           |Communication|                            |      |
|      |                           |   Server    |<-(G)Communication Response-|      |
|      |<-(H)Communication Response|             |                            |      |
|      |                           +-------------+                            |      |
|      |                                                                      |      |
|      |-----------------------(E')Communication Request--------------------->|      |
|      |                                                                      |      |
|      |>----------------------(F')Communication Response---------------------|      |
+------+                                                                      +------+

                   Figure 1: Abstract Protocol Flow

   The abstract flow illustrated in Figure 1 describes the interaction
   between the three roles and includes the following steps:

   (A) The AI Agent B requests to register its capabilities and related
   attributes to Agent Registration Server.

   (B) The Agent Registration Server authenticates the AI Agent B’s
   capabilities and then stores them, e.g., in its local database.

   (C) AI Agent A initiates a capability discover request to the Agent
   Registration Server, the request includes the intent, task or other
   information.




Zhou, et al.              Expires 23 April 2026                 [Page 5]

Internet-Draft          Agent Networks Framework            October 2025


   (D) The Agent Registration Server matches the intent or task with the
   capabilities stored in its local database, and responses with matched
   AI Agents list to the AI Agent A.

   Option1:

   (E) The AI Agent A selects AI Agent B from the list and sends a
   communication request to AI Agent B via Agent Communication Server.

   (F) The Agent Communication Server establishes the session and routes
   the message to AI Agent B.

   (G) The AI Agent B receives the communication request and sends a
   response to the Agent Communication Server.

   (H) The Agent Communication Server transfers the response to the AI
   Agent A.

   Option2:

   (E') The AI Agent A selects AI Agent B from the list and sends a
   communication request to AI Agent B directly.

   (F') The AI Agent B receives the communication request and sends a
   response to the AI Agent A.

4.  Digital Identifier

   The digital identity mechanism is used for the registration,
   discovery and communication flows.

   *  Registration: digital identity contains a global unique identifier
      of AI agent as a basis for authentication and addressing during
      communication flow.  Several agent-related attributes
      (capabilities/skills/services) are contained in the digital
      identity and registered with the identifier at the same time.  The
      related credentials in the digital identity can be used for the
      verification.

   *  Discovery: the registered AI agent can then be discovered by other
      agents based on either identifier or capabilities.  The agent can
      be discovered across different domains.

   *  Communication: one AI agent can communicate with the other AI
      agent, by sending an initial message with the identifier obtained
      from the discovered digital identity.  The network can use this
      identifier for addressing and routing the message to the target AI
      agent.



Zhou, et al.              Expires 23 April 2026                 [Page 6]

Internet-Draft          Agent Networks Framework            October 2025


   *  Authentication: during the communication establishment, both AI
      agents can use the credentials for the identifier for
      authentication.  Attributes can be negotiated after the
      authentication.

   *  Authorization: compared to human communication, AI agent
      communication needs to be explicitly authorized at all time.  The
      attribute-based authorization mechanism can support both direct
      agent-agent authorization and delegated authorization, even for
      the user authorization.

   In order to fulfill the requirements mentioned above, it is suggested
   to introduce the W3C Decentralized Identifier (DID)[DID] and
   Verifiable Credential (VC) [VC_Card]standards as the basic digital
   identity components.

   *  DID: The core DID specification does not require implementers to
      use specific computational infrastructure to build decentralized
      identifiers, allowing us to fully leverage existing mature
      technologies and well-established network infrastructure to build
      DIDs.

   *  VC: The VC can be used as container of attributes of an AI agent.
      The attributes of an AI agent may come from different sources
      which can be verified by the VC.  This will help increase the
      interoperability of cross-domain communications.

5.  Agent Description

   Agent Description (AD) exists in document form.  The AD document
   serves as the entry point to access an agent, functioning similarly
   to a website homepage.  Other agents can obtain information such as
   the agent's name, affiliated entity, functionalities, and interaction
   APIs or protocols from this AD document.  With this information, data
   communication and collaboration between agents can be achieved.

5.1.  Agent Description Document Format

   The Agent Description (AD) document serves as the external entry
   point for an agent and can be provided in either of the following
   formats:

5.1.1.  Natural Language Format

   Leveraging advancements in AI capabilities, the AD document can be
   entirely described using natural language.





Zhou, et al.              Expires 23 April 2026                 [Page 7]

Internet-Draft          Agent Networks Framework            October 2025


5.1.2.  Structured Format

   Since different agents may utilize varying models with distinct
   capabilities, a structured approach is recommended for ensuring
   consistent and accurate interpretation of the same data across
   diverse models.  Structured Format supports multiple document types:

   *  JSON

   *  JSON-LD

   *  Other structured document formats

5.2.  Agent Information Interaction Mechanism

   Agent description documents include the following two types of
   resources:

5.2.1.  Information

   Agents may provide the following types of data:

   *  Textual files (e.g.: .txt, .csv, .json)

   *  Image files (e.g.: .jpg, .png, .svg)

   *  Video files (e.g.: .mp4, .mov, .webm)

   *  Audio files (e.g.: .mp3, .wav, .aac)

   *  Other files

5.2.2.  Interface

   Agent interfaces are categorized into two types:

   *  Natural Language Interface: Enables agents to deliver personalized
      services through natural language interaction, supports human-like
      communication and adaptive responses.

   *  Structured Interface: Facilitates efficient and standardized
      service delivery via predefined protocols, ensures
      interoperability and machine-to-machine automation.








Zhou, et al.              Expires 23 April 2026                 [Page 8]

Internet-Draft          Agent Networks Framework            October 2025


5.3.  Security Mechanism

   Security configuration in Agent Description (AD) documents is
   mandatory.  The security definition must be activated through the
   security member at the agent level.  This configuration constitutes
   the required security mechanism for agent interactions.

   *  Global Scope: When security is declared at the root level of an AD
      document, all resources within the document must enforce this
      security mechanism for access.

   *  Resource-Specific Scope: If security is defined within an
      individual resource, access to that resource is granted only when
      the specified security conditions are met.

   *  Precedence Rule: In cases where resource-level security conflicts
      with root-level security, the resource-level definition takes
      precedence.

5.4.  Integrity Verification

   To prevent malicious tampering, impersonation, or reuse of Agent
   Description (AD) documents, a verification mechanism Proof is
   incorporated into the AD document structure.  The definition of Proof
   shall comply with the specification [VC_Card].

6.  Agent Registration

   Agent Registration Includes the Following Two Modes:

6.1.  Self-Declaration Mode

   In this mode, intelligent agents interconnect externally provided
   resources (including information, interfaces, etc.) using Linked-Data
   technologies, forming a networked ecosystem through agent description
   documents.  Other agents can selectively retrieve appropriate
   resources via metadata described in these agent profile documents.
   Advantages of the Self-Declaration Mode:

   *  Compatibility with Existing Internet Architecture: Facilitates
      search engine indexing of agent-publicized information, enabling
      the creation of an efficient agent data network.

   *  Enhanced Privacy Protection: Pulling remote data to local systems
      for contextual processing mitigates user privacy leakage risks
      inherent in task-delegation models.





Zhou, et al.              Expires 23 April 2026                 [Page 9]

Internet-Draft          Agent Networks Framework            October 2025


   *  Inherent Hierarchical Structure: Supports scalable interactions
      among a large number of agents.

6.2.  Centralized Registration Mode

   In this mode, the AI Agents register their attributes to a
   centralized Agent Registration Server.  The parameters that an Agent
   needs to register in a trust domain (step A in Figure 1) may include:

   *  Name: The name of the Agent, which may not be unique and typically
      represented as a string.

   *  Digital Identifier: The global unique ID of the Agent configured
      by the network provider.

   *  Description: provide a more concise summary of the Agent’s
      relevant details based on natural language.

   *  Address: The access address of the Agent, which might be an URL,
      FQDN, etc.

   *  Version: The current version of the Agent.

   *  Capabilities: The capabilities supported by the Agent, including
      the communication capabilities, interaction modes and multimodal
      capabilities, etc.  The communication capabilities refer to the
      communication protocols supported by the Agent, such as http/2,
      http/3, A2A, ANP, MCP, etc.  The interaction modes may include
      request-response and subscription-notification and others.  The
      multimodal capabilities refer to the data modalities that the
      agent can process, such as text, images, video, real-time audio,
      etc.

   *  Services: The services that the agent can provide.  E.g., AI
      service, sensing service, computing service.

   *  Skills: A list of detailed description of the skills supported by
      the Agent.  The content of each skill includes the name, ID,
      corresponding services, brief abstract, required input, etc.

   *  Interfaces: The APIs interfaces that the agent can provide.

   *  Security related information: For example, the licenses,
      authentication credentials, keys of the Agent.







Zhou, et al.              Expires 23 April 2026                [Page 10]

Internet-Draft          Agent Networks Framework            October 2025


7.  Agent Discovery

   Corresponding to Agent Registration, Agent Discovery Includes the
   Following Two Modes:

7.1.  Proactive Discovery Mode (Corresponding to Self-Declaration Mode)

   In this operational mode, AI agents dynamically acquire Agent
   Description (AD) documents from peer agents through standardized
   discovery protocols (e.g., search engine).  These AD documents serve
   as structured entry points for targeted crawling operations within
   Linked Data networks.  The crawling mechanism implements selective
   resource retrieval, encompassing both semantic information and
   service interfaces, while adhering to ethical crawling policies.

7.2.  Centralized Query Mode (Corresponding to Centralized Registration
      Mode)

   In the mode, the discovery of AI agents depends on the Agent
   Registration Server, and the discovery process consists of two
   phases: "query matching" and "result feedback":

   1) Query Matching Phase:

   The initiating AI Agent A send a Capability Discovery request to the
   Registration Server, and the server screens and matches the target
   agents based on the capability database.  The request parameters
   should be structured (to avoid ambiguous descriptions).  Examples are
   as follows:

   - Requirement description: "Medical image analysis"

   - Location range: "Within 1 kilometer of base station BS-001"

   - Real-time requirement: "Latency ≤ 100ms"

   - Security level: "Medical qualification VC is required"

   The Agent Registration Server matches the requirement with local
   registered Agent description.  After the matching is completed, a
   "target agent list" is generated, which includes Digital Identifier,
   Address, and Capabilities, etc.

   2) Result Feedback Phase:

   The Agent Registration Server feeds back the matched results to the
   initiator AI agent, and the initiator selects a Agent based on the
   results and starts the session establishment with that Agent.



Zhou, et al.              Expires 23 April 2026                [Page 11]

Internet-Draft          Agent Networks Framework            October 2025


8.  Tasks

8.1.  Overview

   The core function of a task is to enable the AI agents involved in
   the communication to agree on "what to do", thereby avoiding
   collaboration failures due to misunderstandings.

   Tasks can be used in capability discovery and communication
   procedures:

   *  Capability Discovery: Obtaining AI agents with matching
      capabilities based on the task descriptions.

   *  Communication: When a Coordinator Agent initiates a communication
      request to an Execution Agent, the request message may carry a
      task description.  In addition, other auxiliary information such
      as images, videos, files, can also be sent along with the task
      description to help accomplish the task.

   An example as shown in Figure 2, a task can be executed by an AI
   agent (e.g., task0 sent to Agent B).  When a complex task is received
   by an AI agent, this task can be broken down into a series of
   subtasks (e.g., task0 broken down to sub-task1 and sub-task2) with a
   clear execution sequence, known as a task chain, and executed by a
   group of AI agents (e.g., sub-task1 sent to Agent B, sub-task2 sent
   to Agent C).  Task chain allows multiple AI agents to execute
   different tasks in a specific sequence based on policy, and enable
   multiple AI agents collaboratively to accomplish a complex task.  The
   Agent communication protocol should support to encapsulate the task
   chain information, e.g., independent with the underlying network
   transport (e.g., IP, MPLS).

   +---------+                                  +---------+
   |         |------------send task0----------->|         |
   | Agent A |                                  | Agent B |
   |         |<-task status/result notification-|         |
   +---------+                                  +---------+

   +---------+                                  +---------+
   |         |-----------send sub-task1-------->|  Agent  |
   |         |<-task status/result notification-|    B    |
   |         |                                  +---------+
   | Agent A |                                  +---------+
   |         |-----------send sub-task2-------->|  Agent  |
   |         |<-task status/result notification-|    C    |
   +---------+                                  +---------+




Zhou, et al.              Expires 23 April 2026                [Page 12]

Internet-Draft          Agent Networks Framework            October 2025


                   Figure 2: Task and Sub-task Assignment

   Tasks can be sent along with the message that establish communication
   session between AI agents, or separately using the established
   session between AI agents.  In the communication session between AI
   agents, one or more tasks can be included, which may be independent
   of each other or associated through context.

   A task is identified by a global unique task ID.  The task ID is
   generated by the agent that creates or assigns the task and are sent
   along with the task to the agent responsible for executing it.

8.2.  Task States

   Based on the length of time to complete the tasks, the task can be
   categorized into:

   *  Short-term tasks: These tasks can be quickly executed and
      completed, often used for simple tasks such as query the weather.

   *  Long-term tasks: These tasks require a longer period of time or
      involve multi-round interaction or extended waiting periods, such
      as writing an article.  During the execution of long-term tasks,
      AI agents can synchronize task states or intermediate results
      among them as needed.

   The task states are maintained by the execution AI agent, and the
   task status can be synchronized among Agents as needed.

8.3.  Task coordination

   The AI Agent communication protocol design MUST consider support for
   Agent Communication Server to facilitate task message forwarding.
   Agent Communication Server SHOULD prioritize message scheduling and
   forwarding based on task requirements to ensure efficient agent
   collaboration and meet transmission QoS objectives.

   This prioritization scheme ensures that critical messages receive
   preferential treatment during congestion or resource contention
   scenarios.

   When delegating tasks to Execution Agents, the Coordinator Agent may
   include task-relevant contextual about the contact information of the
   end user, the task itself, the historical preference information
   known by the Coordinator Agent, and other necessary conversation
   data, to facilitate the task execution.  For example, in trip
   planning case, this may encompass historically booked flight/hotel
   preferences or dynamically perceived context like recent user dialog.



Zhou, et al.              Expires 23 April 2026                [Page 13]

Internet-Draft          Agent Networks Framework            October 2025


   The AI agent protocol should consequently support context sharing
   mechanisms through standardized definitions of context types, length
   constraints, and encoding formats to enhance the effectiveness of
   task execution.

9.  Message mode

   This section defines the message mode of AI agents from two
   dimensions.  One dimension is the number of communication
   participants, which is divided into Point-to-Point Communication (2
   AI agents) and Group Communication (3 or more AI agents), and the
   section is divided into two sub-sections based on this dimension.
   The other dimension is whether the communication between AI agents
   requires the participation of an intermediate node, which divides
   communication into Direct Communication and Indirect Communication,
   and this dimension is further elaborated in the classification within
   each sub-section.

9.1.  Point-to-Point Communication

   Direct Communication: AI agents directly send and receive protocol
   messages without the need for intermediate nodes for processing, or
   AI agents are unaware of these intermediate nodes.  Indirect
   Communication: Communication between AI agents requires processing/
   relaying by the Agent Communication server, and the AI agent must be
   aware of and interact with the Agent Communication server.  The
   function of the Agent Communication server includes but is not
   limited to:

   *  AI agent access control (allowing or blocking an AI agent's
      messages based on its identity or permissions).

   *  Application Layer Proxy (to facilitate monitoring/auditing of AI
      agent communication behavior, or to hide AI agent identity, etc.).

   *  Relay (to forward communication messages, making cross-domain
      communication easier, etc.).

   *  Traffic aggregation (to provide a tree-structured traffic
      regulation, improving communication efficiency).

   *  handle authentication and message relaying between the two
      communicating parties.








Zhou, et al.              Expires 23 April 2026                [Page 14]

Internet-Draft          Agent Networks Framework            October 2025


9.2.  Group Communication

   To better accomplish communication collaboration, agents can
   dynamically form groups.  Information sent by an agent within a group
   can be received simultaneously by other agents in the same group.

9.3.  PUB/SUB Communication

   In this mode, the AI agent sending the information does not know
   which AI agents need to receive it.  It first Publishes the
   information to Agent Communication Server, and this Agent
   Communication Server then distributes the information to the
   subscribing Agents based on their Subscribe status.  Pub/Sub
   communication is a common and efficient method of information
   distribution, especially suitable for large-scale group communication
   scenarios.

10.  Multimodality

   Interactions between AI agents must support multimodality, e.g.,
   text, file, document, image, structured data, real-time audio stream,
   video streaming.  The data size of different multimodality as well as
   the transmission modes (e.g., real-time steaming, or push
   notification) may be different.

   Given these traffic characteristics above, the Agent communication
   protocol should support multimodal data transmission which mentioned
   above.  At the same time, the Agent communication protocol and
   possible protocols of other layers should be designed with the
   principle that the multimodal data can be distinguished and aware,
   based on which they can be handled with differentiated policies for
   better performance assurance and resource efficiency.  For example,
   different multimodal data can be transmitted with different transport
   streams of different quality guarantee.  Or, they can be transmitted
   within a same transport stream but with different policies (e.g.,
   transmission priority).

11.  Session management

   After discovering the peer Agent (e.g., Agent B), the local Agent
   (e.g., Agent A) needs to establish a session with it to communicate.










Zhou, et al.              Expires 23 April 2026                [Page 15]

Internet-Draft          Agent Networks Framework            October 2025


11.1.  Session Establishment and Control

   Before communicating with Agent B, Agent A should first establish a
   secure connection with the Agent Communication Server.  Prior to
   this, Agent A must undergo authentication by the Agent Communication
   Server.  Similarly, Agent B also needs to be authenticated by the
   Agent Communication Server to establish a secure connection.

   Therefore, the Agent Communication Server needs to support the states
   maintenance of the registered Agents, such as the states of Agent A
   and Agent B.

   In order to communicate with Agent B, Agent A initiates a session
   establishment request to the Agent Communication Server.  After
   verifying its permissions, the Agent Communication Server proceeds to
   establish the session, for example, by assigning a globally unique
   Session ID to the new session.  This ID will be used throughout the
   entire session lifecycle to correlate all activities and data.
   Correspondingly, the Agent Communication Server needs to maintain a
   session table, which includes information about all Agents involved
   in the session, especially information about the session initiator.

   Alternately, after authentication and authorization, the Agent A can
   also initial a connection directly to the Agent B.  In this
   situation, the control plane and data plane can be separated.

11.2.  Differentiated QoS Guarantees

   During the session establishment, Agent A can provide the relevant
   QoS requirements for the session.  Consequently, the Agent
   Communication Server can prioritize the processing and forwarding of
   messages according to these requirements to ensure the session's QoS.

12.  Routing

12.1.  Agent ID-based Route look-up

   The scenario described in this section is when an Agent sends a
   message to another Agent (or a group of Agents), and the sending
   Agent knows the recipient Agent's ID or Group ID.  According to the
   two major types of communication modes in Section 6, the situations
   can be classified as follows:

*  Point-to-Point Communication (P2P):

  - In the direct communication mode, the Agent looks up the corresponding IP address using the recipient's ID, thus allowing the message to be sent to the recipient.

  - In the indirect communication mode, the Agent can delegate the ID lookup task to the Agent Communication Server, which is then responsible for sending the message to the recipient Agent based on the ID.



Zhou, et al.              Expires 23 April 2026                [Page 16]

Internet-Draft          Agent Networks Framework            October 2025


*  Group Communication:

  - The Agent delegates the Group ID lookup task to the Agent Communication Server, which is then responsible for sending the message to the recipient Agent in the same group.

12.2.  Semantic-based Route resolution

   The scenario described in this section is when an Agent wants to
   communicate with other Agents that possess a certain capability or
   attribute, but does not yet know their IDs.  In this case, a semantic
   search system is needed to search for the Agent IDs that meet the
   criteria based on the capabilities or attributes described by the
   Agent.  The message is then routed according to the retrieved ID.

13.  Protocol Stack Considerations

   The protocol stack of an AI agent is divided into three functional
   layers: the AI Agent communication protocol layer, the application
   layer, and the transmission layer.  AI Agent applications communicate
   with each other through the interfaces provided by the AI Agent
   communication protocol layer.  The AI Agent communication protocol
   operates above the application layer and has requirements for both
   the application layer and transport layer protocols.

   +--------------------------------------------------------+
   |                  AI Agent Application                  |
   +--------------------------------------------------------+

   +--------------------------------------------------------+
   |  AI Agent Comm Protocol Layer (e.g.,JSON-RPC, JSON-LD) |
   +--------------------------------------------------------+

   +--------------------------------------------------------+
   |            Application Layer (e.g.,HTTP, SIP)          |
   +--------------------------------------------------------+

   +--------------------------------------------------------+
   |         Transmission Layer (e.g.,UDP, TCP, QUIC)       |
   +--------------------------------------------------------+

                  Figure 3: AI Agent protocol stack Layer

   AI Agent Communication Layer: This layer provides the basic
   communication function between Agents, including all the functions
   mentioned above in this draft.

   Application layer: This layer SHALL support the following functions:





Zhou, et al.              Expires 23 April 2026                [Page 17]

Internet-Draft          Agent Networks Framework            October 2025


   *  Support bidirectional full-duplex communication between AI agents,
      meaning that an AI agent can both initiate and receive
      communication requests.  In the same communication session, an
      Agent can send multimodal data as well as receive multimodal data.

   *  Be decoupled from the presentation layer.  For example, after the
      presentation layer chooses to use JSON-RPC protocol, JSON-RPC
      messages MUST support being carried over different application
      layer protocols such as HTTP and WebTransport, etc.

   *  Support a flexible routing mechanism at the application layer,
      including direct routing based on URL querying DNS and segment-
      based routing according to DID.

   *  Support a flexible extension mechanism for protocols to better
      meet the increasingly diverse functional requirements of Agent
      communication.

   Transmission Layer: This layer SHALL provide the following functions:

   *  In mobile scenarios, transport layer should dynamically optimize
      and update QoS parameters according to revised QoS rules.

   *  To achieve multimodal data stream multiplexing, multi-path
      transmission capabilities (i.e., MPTCP, MPQUIC) should be adopted
      to support flexible transmission management of multi-source data
      from agents.

   *  the transport layer should either transmit unfinished data packets
      to the new link or switch data to a backup link, thereby enabling
      mobility management for agent communication.

14.  Security Considerations

   Security of AI agent communication is not detailed in this draft.
   Considering its independence, we suggest that it could be discussed
   separately through other proposals from the following aspects:

   *  Identity: AI agents vary from embodied robots to virtualized
      assistant, which introduces different identity and credential
      storage approach.  The protocol should consider a unified and
      compatibility mechanism to meet these requirements, e.g., SIM-
      based robots, certificate-based AI assistant.

   *  Authentication: AI agents can reuse the authentication mechanism
      provided by the single trusted domain e.g., primary authentication
      between the agent and the core network.  So that the agents may
      simplify the direct authentication process.



Zhou, et al.              Expires 23 April 2026                [Page 18]

Internet-Draft          Agent Networks Framework            October 2025


   *  Authorization: Current practices of agent communication mostly
      rely on existing OAuth 2.0 related mechanism.  It should be
      considered that there will be different authorization mechanisms
      for direct authorization, delegated authorization and user
      authorization.

   *  Cross-domain Security: This draft focused on the communication
      within one trust domain.  However, the cross-domain trust and
      security of AI agents should also be considered in next steps

   *  Discovery Privacy: The publication of an AI agent should get
      owner’s approval.  Not all agent cards/descriptions/identities
      should be published considering the possible sensitive information
      associated with its owner who may be a natural person.

   *  Task Privacy: Agents involved in task execution should follow the
      principle of task description minimization, meaning that each
      agent should only receive the minimum and necessary information
      required to complete its task, in order to prevent unauthorized
      access to sensitive information.  In addition, context sharing may
      impact user privacy, so it is important to consider limitations on
      the scope of context sharing, especially for sensitive information
      such as the user's name, age, and address.

15.  IANA Considerations

   TBD.

16.  Conclusions

   This framework focuses on AI agent communication within a single
   trust domain, introducing the communication framework, basic
   processes, and key mechanisms.  Considering that multiple trust
   domains may exist in practical deployments, the mechanisms such as
   digital identity format, capability registration and discovery, and
   routing involved in cross-domain scenarios may differ from those
   within a single trust domain.  Therefore, further research on cross-
   domain agent communication is needed in the future.

17.  Acknowledgements

   TBD

18.  References

18.1.  Normative References





Zhou, et al.              Expires 23 April 2026                [Page 19]

Internet-Draft          Agent Networks Framework            October 2025


   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

18.2.  Informative References

   [ANP]      "Agent Network Protocol 1.0", 15 April 2024,
              <https://github.com/agent-network-protocol/
              AgentNetworkProtocol>.

   [I-D.stephan-ai-agent-6g]
              Stephan, E., Schott, R., Lopez, D., Duan, X., and L.
              Morand, "AI Agent protocols for 6G systems", Work in
              Progress, Internet-Draft, draft-stephan-ai-agent-6g-00, 7
              July 2025, <https://datatracker.ietf.org/doc/html/draft-
              stephan-ai-agent-6g-00>.

   [VC_Card]  "Verifiable Credential Data Integrity 1.0", 15 May 2025,
              <https://www.w3.org/TR/vc-data-integrity/#defn-domain>.

   [DID]      "Decentralized Identifiers v1.1", 18 September 2025,
              <https://www.w3.org/TR/did-1.1/>.

Authors' Addresses

   Ye Zhou
   ANP Open Source Community
   No. 188, Zongguantang Road, Gusu District
   Suzhou, Jiangsu Province
   China
   Email: zynetzy1@aliyun.com


   Kehan Yao
   China Mobile
   Email: yaokehan@chinamobile.com


   Menghan Yu
   China Telecom
   Beiqijia Town, Changping District
   Beijing
   China



Zhou, et al.              Expires 23 April 2026                [Page 20]

Internet-Draft          Agent Networks Framework            October 2025


   Email: yumh1@chinatelecom.cn


   Mengyao Han
   China Unicom
   No.9, Shouti South Road, Haidian District
   Beijing
   China
   Email: hanmy12@chinaunicom.cn


   Cheng Li
   Huawei
   Email: c.l@huawei.com





































Zhou, et al.              Expires 23 April 2026                [Page 21]
