



Network Working Group                                            Y. Tong
Internet-Draft                                                    A. Sun
Intended status: Informational                                   A. Wang
Expires: 6 May 2026                                               Y. Liu
                                                                  Y. Xie
                                                           China Telecom
                                                         2 November 2025


           Network AI Agent Use Cases and Requirements in 6G
              draft-tong-network-agent-use-cases-in-6g-00

Abstract

   This draft introduces use cases related to network AI agents in 6G,
   with a focus on the interaction workflows of network AI agents in two
   distinct scenarios: connectivity services and third-party application
   services.  These use cases primarily draw upon 6G-related scenarios
   outlined in the 3GPP technical report [TR22.870].  Furthermore, the
   document elaborates on the integration of network AI agents within
   the 6G framework and discusses corresponding network requirements.

Status of This Memo

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   This Internet-Draft will expire on 6 May 2026.

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   and restrictions with respect to this document.  Code Components
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
   2.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Generative Network for Connectivity Services  . . . . . .   3
       2.1.1.  Process Flow  . . . . . . . . . . . . . . . . . . . .   3
       2.1.2.  Analysis of AI Agent Roles  . . . . . . . . . . . . .   5
     2.2.  Network Capability Exposure to Third-Party Services . . .   5
       2.2.1.  Process Flow  . . . . . . . . . . . . . . . . . . . .   5
       2.2.2.  Analysis of AI Agent Roles  . . . . . . . . . . . . .   6
   3.  Architecture and Potential Requirements of AI Agent-Integrated
           6G Network  . . . . . . . . . . . . . . . . . . . . . . .   7
     3.1.  AI Agent-Integrated 6G Network Framework  . . . . . . . .   7
     3.2.  Potential Network Requirements  . . . . . . . . . . . . .   8
   4.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   8
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   9

1.  Introduction

   In the era of 6G network, the emergence of new services such as AI,
   computing, and sensing poses unprecedented challenges to network
   processing capabilities.  As autonomous decision-making entities, AI
   agents are poised to become a core driving force for innovation in
   network architecture.  Through capabilities such as intent
   understanding, environmental modeling, and cross-domain
   collaboration, they can dynamically adapt to user demands and network
   operating conditions, facilitating an intelligent leap toward
   "proactive optimization" in network.  Within the 3GPP R20 6G
   standardization research, AI agents have already been taken into
   consideration.  At the recent SA1#111 meeting, a total of 41
   proposals related to AI agents were submitted, including 18 new use
   cases, of which 6 were ultimately approved.  This paper primarily
   focuses on how network intelligence can enhance internal quality
   while simultaneously enabling external empowerment.  Specifically, AI
   agents play a dual role in 6G network: on the user and operator
   sides, they provide low-latency, high-reliability communication
   services based on real-time behavior prediction and resource



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   scheduling, thereby improving network quality and user experience;
   for personalized requests from third-party applications, they expose
   network capabilities externally through AI agents, enabling external
   empowerment and fostering innovative scenarios such as smart
   healthcare IoT and immersive metaverse applications.  Section 2
   summarizes two categories of relevant cases, Section 3 describes the
   network framework and requirements, and Section 4 concludes this
   draft.

1.1.  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.  Use Cases

   In scenarios involving connectivity services, users or network
   administrators submit intent-based requests to the 6G network.  The
   network agent can perform accurate intent parsing, conduct task
   classification and planning, and delegate further strategy
   formulation and execution to hierarchical service agents, thereby
   completing network design in accordance with the service intent.  For
   new service scenarios such as AI, the network agent can provide AI
   capabilities from the network side based on the given intent.

2.1.  Generative Network for Connectivity Services

   Based on service requirements or network status, users or network
   administrators can submit networking intents to the network.  These
   intents are then fulfilled by agents within the network, which
   perform cross-domain collaborative optimization and configuration of
   resources to achieve efficient generative networking.

2.1.1.  Process Flow

   1.  David, a network administrator from a certain operator, submits
       the following request to the network: "A concert will be held at
       the downtown stadium tomorrow from 19:00 to 22:00.  Please design
       the network to smoothly handle the traffic surge while ensuring
       the fluency of key user services."

   2.  The 6G core network Task Orchestration Agent parses the
       networking intent requirement, performs an initial task
       breakdown, splits the task, and identifies the agents required
       for execution.  For example, the task is decomposed into multiple



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       subtasks, including intelligent access, resource configuration,
       service identification, and service policy optimization,
       involving the Connectivity Agent and the QoS Assurance Agent.

   3.  The Task Orchestration Agent acquires the data necessary for task
       execution to support decision-making by the service agents.  For
       instance, it can invoke external tools to obtain historical data
       and predict the service scale for this networking task through
       intelligent forecasting and digital twin functionalities.  The
       subtask information is then distributed to the corresponding
       agents.  This information includes task descriptions, task
       identifiers, task requirements, and task data, supporting the
       delivery of structured data, intents, control commands, and other
       types of information to fully convey task instructions.

   4.  Upon receiving the intelligent access and resource configuration
       subtasks, the Connectivity Agent addresses user access requests
       within the area by employing intelligent access algorithms to
       schedule corresponding 6G network elements for efficient user
       access.  Based on the distribution of user types and services in
       the area, it utilizes a reinforcement-based generative policy
       model to allocate and optimize network resources.  During the
       decision-making process, in addition to using the data provided
       in the task information, the agent also interacts with network
       elements to obtain real-time data and performs enhanced data
       analysis.

   5.  The QoS Assurance Agent receives the subtasks for service
       identification and service policy optimization.  During the
       networking optimization task, the QoS agent activates the service
       identification function, providing a service basis for generating
       networking strategies through service recognition and quality
       degradation analysis.  According to the real-time status of
       services, it dynamically generates core network optimization
       strategies.  Meanwhile, leveraging its cross-domain coordination
       capability, it shares strategies with the RAN side to achieve
       refined resource allocation and optimize service quality.

   Based on the task agents' demand analysis and task planning, the
   Connectivity Agent and the QoS Assurance Agent, through intelligent
   access, dynamic resource configuration, precise service
   identification, quality degradation analysis, and leveraging policy
   generation capabilities, achieve efficient generative networking,
   thereby ensuring the highly effective execution of the task.







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2.1.2.  Analysis of AI Agent Roles

   This case involves two types of agents.  The first type is the
   central agent, which is responsible for receiving requests from users
   or network administrators, performing intent recognition, extracting
   detailed information, and decomposing tasks.  The second type of
   agent is oriented toward specific services and is responsible for
   task decomposition, policy generation, and execution.

   In this specific case:The role of the central agent is undertaken by
   the Task Orchestration Agent, which performs intent recognition on
   the network administrator's request, divides it into multiple
   subtasks, and extracts key task information to distribute to the
   corresponding second-type agents.

   The second-type agents are service agents, responsible for policy
   planning and execution of specific subtasks.  In this scenario, two
   service agents are involved: the QoS Assurance Agent and the
   Connectivity Agent.  Each agent, based on the subtask it receives,
   leverages its corresponding capabilities to collaboratively
   accomplish the networking task.

2.2.  Network Capability Exposure to Third-Party Services

   The concert organizer initiates a high-value user recommendation
   request to the network via its official "AR Smart Interaction" app.
   The network then performs statistical analysis based on user contract
   data and service profiles, delivers the corresponding capability, and
   assists the third party in formulating a targeted push notification
   strategy for the app.

2.2.1.  Process Flow

   1.  During the concert, the organizer provides an immersive
       interactive service through the official "AR Smart Interaction"
       app.  This third-party app platform sends an intent request to
       the Capability Exposure Agent to acquire user information for
       delivering customized services.  For example, a request may
       state: "Please provide me with relevant information for the users
       on the list, including user profiles, network quality, and
       service resource allocation, to facilitate customized services."
       Additionally, the Capability Exposure Agent also supports
       receiving specified data requests from the third-party app
       platform via standardized APIs.

   2.  The 6G core network Capability Exposure Agent parses the intent
       conveyed by the third-party app platform and decomposes it into
       tasks directed at the Service Customization Agent.  These tasks



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       may include obtaining target user preferences, identifying high-
       demand users within the target group, and acquiring the network
       quality of target users.  The Capability Exposure Agent then
       dispatches these tasks to the Service Customization Agent.

   3.  Upon acquiring task information, the Service Customization Agent
       invokes data functions to retrieve the required analysis results.
       This retrieval is performed either from its internal data storage
       or by obtaining results from other agents and network elements.

   4.  If existing data cannot fully meet the application requirements,
       the Service Customization Agent can also retrieve raw data and
       utilize its built-in functions, such as user profiling and
       network quality assessment, to generate up-to-date analysis
       results.  For instance, it may interact with the Connectivity
       Agent to obtain network quality data for relevant users, which is
       then processed by its network quality assessment function to
       produce the required results.

   5.  After gathering all necessary information for the task, the
       Service Customization Agent reports the results back to the
       Capability Exposure Agent.

   6.  The Capability Exposure Agent then provides the requested
       information to the "Concert AR Smart Interaction" app platform
       via standardized APIs.

   7.  With the received data, the "Concert AR Smart Interaction" app
       platform can deliver customized services based on its own
       business logic.  Examples include selecting preferred interaction
       methods according to user profiles, recommending services likely
       to interest the user, and suggesting higher-quality service
       packages to high-value users.

2.2.2.  Analysis of AI Agent Roles

   This case involves two categories of agents.  The first category is
   the central agent, responsible for receiving requests for third-party
   personalized services, performing intent recognition, extracting
   detailed information, and conducting task decomposition.  The second
   category comprises service agents, which are oriented toward specific
   services and handle task decomposition, policy generation, and
   execution.








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   In this specific case:The role of the central agent is fulfilled by
   the Capability Exposure Agent, which performs intent recognition on
   requests from third-party applications and distributes subtasks to
   the Service Customization Agent.The Service Customization Agent is
   responsible for the analysis, execution, and feedback of the specific
   subtasks.

3.  Architecture and Potential Requirements of AI Agent-Integrated 6G
    Network

3.1.  AI Agent-Integrated 6G Network Framework

   The 6G network framework incorporating network AI agents is primarily
   structured into three layers:

   1.  The first layer is the Central Intelligence Layer, composed of
       the Task Orchestration Agent and the Capability Exposure Agent.
       The Task Orchestration Agent is responsible for receiving
       requests from the user side and the network management side.  The
       Capability Exposure Agent handles capability exposure requests
       from external applications.

   2.  The second layer is the Service Agent Layer, which consists of
       multiple service agents, including but not limited to the QoS
       Assurance Agent, Connectivity Agent, and Service Customization
       Agent.  Each service agent is responsible for the analysis,
       decision-making, and execution of its corresponding tasks.
       During operation, service agents need to communicate with network
       elements and external tools to collaboratively complete their
       assigned tasks.

       *  The functionalities of the QoS Assurance Agent may include
          service identification, quality degradation analysis, and
          dynamic parameter adjustment.

       *  The functionalities of the Connectivity Agent may include
          intelligent access, mobility management optimization, and
          resource configuration.

       *  The functionalities of the Service Customization Agent may
          include user profiling, network quality assessment, and
          service orchestration.

       By leveraging their respective capabilities, the service agents
       work in coordination to accomplish specific tasks.






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   3.  The third layer is the Atomic Capability Layer, which comprises
       network elements and tools.  This layer provides the fundamental
       capabilities required for the service agents to execute their
       various tasks.

3.2.  Potential Network Requirements

   Based on the above case studies and framework, the network side must
   possess the following capabilities:

   1.  The network must support the reception of intent-based requests,
       such as user-side intents, network management intents, and third-
       party requests for capability exposure.  The central-layer agents
       must be capable of identifying and parsing these intents.

   2.  The network must support communication protocols between AI
       agents, enabling direct interaction among network AI agents to
       collaboratively complete tasks.

   3.  The network must support communication protocols between AI
       agents and external tools or network elements.  During task
       execution, service agents need to invoke network elements or
       specific tools to fulfill their responsibilities.

   4.  The network must possess identity management capabilities for
       network AI agents.  For instance, the Task Orchestration Agent
       should manage the identities and capabilities of other agents to
       assign specific subtasks to the appropriate agents.  Similarly,
       the Connectivity Agent should be able to manage the identities of
       external AI agents, ensuring their secure access and supporting
       on-demand networking, among other requirements.

   5.  The network must enable the exposure of network AI agent
       capabilities to external parties, thereby supporting innovative
       third-party services.

4.  Conclusion

   In 6G network, AI agents can function both as communication entities
   and as tools for empowering the network.  This chapter analyzes the
   potential benefits of introducing AI agents into 6G through two types
   of case studies.  It also examines how network AI agents can be
   integrated into the 6G architecture and identifies the corresponding
   network requirements.







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5.  IANA Considerations

   This memo includes no request to IANA.

6.  Security Considerations

   This document should not affect the security of the Internet.

7.  References

7.1.  Normative References

   [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/rfc/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/rfc/rfc8174>.

7.2.  Informative References

   [TR22.870] 3GPP TR 22.870, "Study on 6G Use Cases and Service
              Requirements", 2025.

Authors' Addresses

   Ying Tong
   China Telecom
   Beiqijia Town, Changping District
   Beijing, 102209
   China
   Email: tongy@chinatelecom.cn


   Ao Sun
   China Telecom
   Beiqijia Town, Changping District
   Beijing, 102209
   China
   Email: suna1@chinatelecom.cn









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   Aijun Wang
   China Telecom
   Beiqijia Town, Changping District
   Beijing, 102209
   China
   Email: wangaj3@chinatelecom.cn


   Yang Liu
   China Telecom
   Beiqijia Town, Changping District
   Beijing, 102209
   China
   Email: liuyang19@chinatelecom.cn


   Yuanbao Xie
   China Telecom
   Huajing NewTown，Tianhe District
   Guangzhou
   Guangdong, 510630
   China
   Email: xieyuanb@chinatelecom.cn




























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