



Network Management Research Group                                 C. Guo
Internet-Draft                                              China Mobile
Intended status: Informational                                 July 2025
Expires: 8 January 2026


     Large Model based Agents for Network Operation and Maintenance
                  draft-chuyi-nmrg-ai-agent-network-01

Abstract

   Current advancements in AI technologies, particularly large models,
   have demonstrated immense potential in content generation, reasoning,
   analysis and so on, providing robust technical support for network
   automation and self-intelligence.  However, in practical network
   operations, challenges such as system isolation and fragmented data
   lead to extensive manual, repetitive, and inefficient tasks, the
   improvement of intelligence level is very necessary.  This document
   identifies typical scenarios requiring enhanced intelligence, and
   explains how AI Agents and large model technologies can empower
   networks to address operational pain points, reduce manual efforts,
   and explore impacts on network data, system architectures, and
   interfaces correspondingly.  It further explores and summarizes
   standardization efforts in implementation.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

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
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   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 2 January 2026.




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Copyright Notice

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   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Large Models  . . . . . . . . . . . . . . . . . . . . . .   3
     1.2.  AI Agent  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Acronyms & Abbreviations  . . . . . . . . . . . . . . . . . .   5
   3.  Use case  . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     3.1.  Scenario 1: Network Migration Operations  . . . . . . . .   5
     3.2.  Scenario 2: Network Fault Handling  . . . . . . . . . . .   6
     3.3.  Scenario 3: Intelligent Assistant of User Complaint
           Handling  . . . . . . . . . . . . . . . . . . . . . . . .   7
   4.  Architecture and Functionality  . . . . . . . . . . . . . . .   8
   5.  Observed Requirements . . . . . . . . . . . . . . . . . . . .  10
     5.1.  Data Requirement  . . . . . . . . . . . . . . . . . . . .  10
     5.2.  Standardized Atomic Capabilities Interface  . . . . . . .  12
     5.3.  Network Intent Recognition Requirement  . . . . . . . . .  12
     5.4.  Self Close Loop Requirement . . . . . . . . . . . . . . .  13
     5.5.  Resource Requirement  . . . . . . . . . . . . . . . . . .  13
     5.6.  Muti-Agent Collaboration Requirement  . . . . . . . . . .  14
     5.7.  Security Requirement  . . . . . . . . . . . . . . . . . .  15
   6.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  15
     6.1.  Informative References  . . . . . . . . . . . . . . . . .  15
     6.2.  Normative References  . . . . . . . . . . . . . . . . . .  15
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  16

1.  Introduction











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1.1.  Large Models

   Large models refer to AI systems based on deep learning techniques,
   containing massive parameters (typically billions to trillions).  It
   is trained on large-scale datasets, and is capable of capturing
   complex patterns and associations, demonstrating outstanding
   abilities in natural language processing, image generation, decision-
   making, and reasoning.

   Recent breakthroughs in models like GPT-4 and DeepSeek have
   continuously pushed technical boundaries and enhancing the
   performance of models.Users can use the capabilities of large models
   by accessing or deploying inference models, and combining with Fine
   tuning, Prompt Learning, etc.

   The big model has been empowered in multiple vertical domains, like:

   *  Research: AlphaFold for protein structure prediction, Galactica
      for scientific paper assistance.  Industry: Generative design
      (e.g., automotive/chip architecture optimization), automated code
      development (GitHub Copilot).

   *  Finance: Risk prediction, automated report generation.

   In the future, large models will also move towards embodied AI ,
   embedding model capabilities into physical terminals such as robots
   and autonomous driving, continuously building an open-source
   developer ecosystem, opening up some model capability interfaces, and
   promoting industry collaborative innovation.

1.2.  AI Agent

   Intelligent agent, as an important concept in the field of artificial
   intelligence, refers to a system that can autonomously perceive the
   environment, make decisions, and execute actions.  It has basic
   characteristics such as autonomy, interactivity, reactivity, and
   adaptability, and can independently complete tasks in complex and
   changing environments.  Intelligent agents have the ability to learn
   and make decisions.  Through learning algorithms and data analysis,
   they can extract useful information from massive amounts of data and
   form their own knowledge base.  In the decision-making process,
   intelligent agents can comprehensively consider various factors and
   use methods such as logical reasoning and probability statistics to
   make the optimal decision.  This ability gives intelligent agents a
   significant advantage in solving complex problems.

   There are four design patterns for intelligent agent workflow:




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   *  Reflection: Let the agent review and revise the output generated
      by themselves;

   *  Tool Use: LLM generates code, calls APIs, and performs practical
      operations;

   *  Planning: Let the agent decompose complex tasks and execute them
      according to the plan;

   *  Multi-agent Collaboration: Multiple agents play different roles
      and collaborate to complete tasks.

   At present, intelligent agents have been used in the following
   scenarios:

   *  Personal assistant:

      -  Cross platform task agent: Automatically organize emails,
         schedule meetings, and manage schedules (such as Microsoft
         Copilot).

      -  Life Butler: Adjust smart homes according to user habits and
         recommend personalized health plans.

   *  Industry Intelligence:

      -  Financial advisory: Real time analysis of market data,
         generation of investment portfolio recommendations, and
         automatic execution of trades.

      -  Medical diagnosis: Provide dynamic treatment recommendations
         based on the patient's medical history and real-time monitoring
         data.  Industrial operation and maintenance: Predicting
         equipment failures and scheduling maintenance resources to
         optimize production line efficiency.

   *  Virtual world interaction:

   *  -  Game NPC: Intelligent characters with emotions and memories
         (such as AI driven open world NPCs).

      -  Metaverse Guide: Help users explore virtual spaces and provide
         personalized content recommendations.

   *  Scientific research:






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   *  -  Laboratory assistant: Automatically design experiments, analyze
         data, and propose hypotheses (such as chemical synthesis
         agents).

      -  Climate simulation: Coordinating multidimensional data models
         to predict extreme weather and generate response plans.

2.  Acronyms & Abbreviations

   Large model:  Machine learning models with large-scale parameters and
      computing power are typically constructed from deep neural
      networks, containing billions or even hundreds of billions of
      parameters, capable of understanding text, images, speech, and
      other content, and performing tasks such as text generation, image
      generation, inference question answering, and scientific
      prediction.

   AI Agent:  An AI agent is an intelligent entity with autonomous
      perception, decision-making, and execution capabilities, driven by
      goals in dynamic environments.

3.  Use case

   This section list 3 typical use cases of how AI Agent can facilite
   network operation and maintenance, which have been proven to be the
   most effective combination in the practice.

3.1.  Scenario 1: Network Migration Operations

   The current network undergoes a large number of service migration or
   device switchover every day/month, which have a high degree of
   similarity in steps and processes, involving querying and filling a
   large amount of data and configuration.  There are two typical types
   of migrations: service provisioning (for external service data
   configuration) and migration change (for internal tasks such as route
   publishing and network optimization).  Large models naturally have
   the ability to process and recognize massive amounts of data, and
   intelligent agents can guide the process of each step like
   experienced experts.

   Automation via large models and agents can reduce errors and free
   human resources.  Key tasks include:

   *  Migration Plan Generation: Designing workflows and deployment
      strategies.

   *  Plan Auditing: Checking configurations, compliance, and correcting
      errors (e.g., typos, hallucinations).



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   *  Automated Execution: Replacing manual configurations with AI-
      generated scripts, call corresponding systems to finish tasks.

   Taking the service provisioning scenario as an example, typically,
   when doing migration, it was necessary to manually log in the device
   configuration parameters.  Now, through the interaction of the large
   model, the large model generates a script to distribute the device,
   also configure and audit it.  The agent can call other systems, such
   as digit twin platform for script testing, view the impact of the
   changed parameters, and return to the assigned system to reduce
   manual errors.  Finally, based on the analysis of the results, it can
   achieve automatic distribution when there shows no problem.

3.2.  Scenario 2: Network Fault Handling

   Traditional fault handling is usually single domain autonomy.  When
   monitoring detects anomalies such as alarms, a fault ticket is
   generated and sent to the corresponding specialized network domain
   for solutions.  Generally speaking, one single fault point triggers
   alarms across multiple domains and locations, tickets will be
   assigned to each involved domain, this results in numerous tickets
   for each domian, which leads to low processing efficiency.

   Different network domains receiving fault tickets will conduct fault
   analysis, positioning, and report troubleshooting results to the
   upper monitoring layer for fault management.  Each network domain has
   its own troubleshooting process for different alarms.  Based on
   expert experience, data analysis, and tools, the positioning
   capability is very comprehensive and reliable, but it heavily relies
   on manual labor and may requires night duty to respond promptly when
   faults occur.  By training professional fault intelligent agents with
   fault cases and manually summarized experience, coupled with fine-
   tuning of professional domain knowledge, domain-specific fault
   handling agents can identify faults based on alarm types and other
   information, generate troubleshooting steps (such as data query and
   tool calling schemes), and call corresponding APIs to perform
   troubleshooting actions, finally obtaining fault positioning
   conclusions.

   Due to the chain reaction caused by alarms, which usually involve
   multiple specialties, top-level monitoring agents need to cooperate
   and coordinate with various domain agents, and positioning conclusion
   needs to be carried out across specialties.  In addition to replacing
   manual alarm preprocessing and ticket dispatching, agents also need
   to be connected to alarm systems, performance systems, etc., to
   perceive abnormal data in the network at any time, trigger fault
   analysis and positioning processes, and continuously interact with
   different domain agents for necessary information, find the root



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   cause of the fault, and finally generate the fault handling report.
   The top-level monitoring agent has different domains’ alarm, topology
   and other information, and can conduct cross-disciplinary joint
   consultations, which is more advantageous than traditional methods.

3.3.  Scenario 3: Intelligent Assistant of User Complaint Handling

   In the past few years, there has been research and development of AI
   model capabilities in the field of complaints, such as complaint
   prediction, complaint problem positioning, and complaint warning, to
   improve complaint handling efficiency, reduce complaint ratios, and
   play an auxiliary role in various service complaint handling such as
   home broadband, 4/5G, and IoT.  However, this single funtion models
   lack natural language processing and content generation capabilities,
   and their role in improving efficiency is limited.

   The complaint handling faces challenges such as heavy workload and
   lack of pre-processing ability in the front-line customer service.
   Taking the scenario of home broadband as an example, traditional
   processes allow users to seek help from manual customer service by
   making phone calls when encountering problems.  Frontline customer
   service staff will answer the user's questions and provide guidance
   on simple handling.  If it cannot be quickly resolved through simple
   steps, a problem handling ticket will be generated, and the system
   will assign maintenance personnel to come to the site.  After the
   repair is completed, a dedicated person will call for satisfaction
   follow-up.  Traditional processes rely heavily on manual labor, and
   customer service responses have a high degree of repetition and
   structure.  It is necessary to introduce electronic means to
   systematically improve processing efficiency and replace manual
   labor.

   Introducing the intelligent assistant of user complaint handling can
   replace customer service staff in answering customer complaint calls,
   it can achieve the following process:

   *  Firstly, perform intent recognition on the voice content of the
      customer's phone call

   *  Intelligent diagnosis of identified problems, determining the
      category and attributes of the problem, and determining the next
      steps for operation

   *  Can provide direct response/prompt handling, dispatch, remote
      operation, and manual processing for user issues

   *  Intelligent quality inspection and automatic satisfaction follow-
      up



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   Leveraging the complaint-handling intelligent agent and its AI-
   powered diagnostic engine and big data analytics capabilities, we
   have built an end-to-end service loop of "proactive perception -
   precise prediction - preemptive resolution".  This transforms
   complaint handling from passive acceptance to active intervention.

   Based on real-time network quality monitoring and a historical fault
   signature database, installation/maintenance technicians can now
   remotely automate issue resolution and generate solutions before on-
   site visits.  The complaint-handling process shifts from human-driven
   to AI-driven, task scheduling evolves from reliance on manual
   experience to autonomous planning and decision-making, and manual
   operations are upgraded to automated execution or human-machine
   collaboration.  This significantly reduces processing time and lowers
   repeat complaint rates, reshaping service experiences through
   intelligent solutions and substantially improving fault resolution
   efficiency.

   During user interactions or technician on-site visits, the system
   utilizes large model capabilities to call APIs or execute SQL queries
   for real-time device status and performance metrics, replacing fixed
   interfaces and manual queries with intelligent integrated access.
   For complex issues and workflows, the large model-based agent can
   also invoke small model capabilities (e.g., network traffic fault
   diagnosis models) mid-process.

   When handling complaints, the agent responsible for direct human-
   complaint interaction can collaborate with other specialized agents
   (e.g., a Home Broadband Troubleshooting Assistant) through multi-
   agent cooperation to achieve closed-loop resolution.

4.  Architecture and Functionality

   Intelligent agents based on large models can automate network
   operations by coordinating system scheduling and leveraging diverse
   capabilities of large models.  This process involves multiple
   interactions with systems such as large models and network management
   systems.  Each agent has specialized functions, such as agents for
   intent understanding or agents dedicated to fault localization and
   demarcation in specific network scenarios.  Current operational
   systems already provide basic data support, foundational atomic
   capabilities, and well-defined orchestration workflows for task
   execution.  However, most processes are manually connected, involve
   repetitive mechanical work, and lack an intelligent coordination
   "brain".  See Figure 1.






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                        Agents                                       Network
+------------------------------------------------------+ +---------------------------+
|                                                      | |                           |
|                    +------------+                    | |Network Systems & Platforms|
|                    | Perception |                    | |                           |
|                    +------+-----+                    +->        AI Models          |
|                           |                          | |                           |
|                  +--------v--------+                 | |    Atomic Capabilities    |
|  +----------+    |      Brain      |    +----------+ | |                           |
|  | Planning <+-+-+                 +-+-+>  Action  | | |          Tools            |
|  +----------+    | LLM | LVM | LSM |    +----------+ <-+                           |
|                  +------+--^-------+                 | |           Data            |
|                         |  |                         | |                           |
|                    +----v--+----+                    | |                           |
|                    |   Memory   |                    | |                           |
|                    +------------+                    | |                           |
+------------------------------------------------------+ +---------------------------+

          Figure 1: Architecture of Large Model based Agents

   Functions of Agents:

   *  Intent Recognition: Understand and interpret user input
      intentions.  Determine whether subsequent tasks require
      identifying suitable agents or multi-turn dialogues to complete
      intent recognition and parsing.

   *  Intent Classification and Analysis: Decompose tasks based on
      recognized user intent.Categorize tasks according to different
      functional requirements.

   *  Perception: Proactively receive alarms, threshold-exceeding
      notifications, or environmental change information, issuing
      warnings when necessary.Accept task requests from other systems,
      potentially involving multimodal data processing.

   *  Memory:

      -  Long-term memory: Stores user habits, domain-specific
         processing experiences (e.g., failure/success cases,
         encountered faults) in knowledge bases.

      -  Short-term memory: Caches temporary processing data (e.g.,
         context).

   *  Agents perform reflection and error correction by interacting with
      long-term memory and contextual information.




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   *  Planning: Analyze and decompose intent based on task objectives
      and learned knowledge.  Orchestrate subtasks (e.g., breaking
      complex problems into simpler ones).  Identify required system
      components (other agents, large models, APIs, etc.).

   *  Decision-Making: Finalize execution plans and match workflows to
      current tasks.  Generate instantiated, executable solutions by
      aligning system components, data, and model strategies.

   *  Execution: Convert orchestrated results into network-
      understandable commands.  Execute tasks by mobilizing resources
      and dynamically adjusting based on feedback.

   *  Multi-Agent Collaboration:

      -  Team Collaboration: Enable coordinated teamwork among multiple
         agents.

      -  Competitive Collaboration: Manage competitive relationships to
         avoid efficiency loss.

5.  Observed Requirements

   Based on the aforementioned practical use cases regarding the
   implementation of intelligent agent functional modules, this chapter
   will summarize and analyze the technologies, challenges and
   constraints encountered during the deployment and implementation of
   agents in network operations.  It will also propose potential
   requirements across different dimensions.

5.1.  Data Requirement

   Data is the foundation of agents to take effect, which includes:

   *  Data used for training and inference: The data used for training
      and inference, which is typically used before the agent provides
      services to users, it can be expert knowledge in operation and
      maintenance processes, logs, configuration rules, policy
      knowledge, case manuals, alarms, network topologies, fault
      reports, and more.  The data here is used to teach big models how
      to do it, usually historical data.  The performance of the model
      based on data learning can be adjusted through reward functions,
      etc.  The actual implementation cases in the later stage can also
      be stored in long-term storage as reinforcement learning, which
      can be used as a reference for generating solutions later.






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   *  Dynamic data during task execution: Dynamic data can also be
      categorized into two types: User intent data (e.g., natural
      language task descriptions), operational data from systems.  For
      user-expressed intents, typically extracted during user
      interactions (which may include multimodal inputs like voice or
      images), agents expose dedicated interfaces for user input, and
      upon receiving it, performs intent recognition before executing
      subsequent steps.  The data used for system operation refers to
      the constantly collected data in the network, such as monitoring
      performance indicators, alarms and other data, as well as messages
      from other systems, such as device network access/upgrade
      information.

   Requirements:

   For training data:

   *  It mainly reflects integrity.  The training data of a large model
      not only needs to reach a certain number of parameter levels, but
      also needs to ensure comprehensive and accurate coverage, reducing
      interference data.

   *  In order to enable intelligent agents to trigger subsequent
      processing based on alarm and other data in the system, it is
      necessary to manually establish expert experience and rules, and
      even establish new interfaces to support the problem meanings
      represented by different data phenomena.  This part can be
      standardized.

   For dynamic data:

   *  Modal transformation.  For example, if it is an LLM based agent,
      users need to convert text before extracting intent when receiving
      images.

   *  How to extract intent.  This requires understanding the running
      data, knowing what the indicators represent, which type of
      analysis and processing should be triggered (consistent with the
      training data requirement 2 mentioned above), and extracting which
      type of features from the input for subsequent intent recognition.
      This process requires classifying the network's events, behaviors,
      etc. in advance.

   *  Align the formats of different input data.  The main solution is
      to solve the problem of not being able to directly understand and
      call each other when different APIs and tools are accessed or
      called with different formats.  The MCP protocol in the industry
      can ease this problem.  MCP converts tool capabilities into



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      standardized function semantics through JSON Schema, enabling
      large models to implement zero sample calls based on tool
      descriptors without the need for prior learning or fine-tuning.
      This enables the integration of large language models with
      external data sources and tools, and is used to establish secure
      bidirectional connections between large models and data sources.
      MCP defines a set of universal communication protocols, data
      formats, and rules that can be simplified for development,
      flexible, real-time responsive, secure, compliant, and scalable.
      It processes both local resources (such as databases, files,
      services, etc.) and remote resources (such as APIs like Slack or
      GitHub) through the same protocol, hence it is known as the USB-C
      port for AI applications.  In addition to MCP, enterprises can
      also use their own agent protocols to call different systems and
      adopt and design unified format conversion standards.

5.2.  Standardized Atomic Capabilities Interface

   Atomic capability refers to a series of orchestrated workflows
   designed to accomplish a subtask.  It encapsulates various APIs,
   exposes a unified interface and capabilities externally, and serves
   as the minimal functional unit for achieving specific subtasks.
   Atomic capabilities can be defined with standardized inputs and
   outputs to facilitate cross-system communication and calls.

   In the process of network operation and maintenance, in order to
   ensure reliability and the interconnection and calling of different
   capabilities, it is necessary to standardize the input and output of
   the minimum set of capabilities in the network for intelligent agents
   to understand and call.  For example, completing the delimitation and
   localization of a certain type of fault involves multiple steps and
   links.  In practical use, the implementation and logic of the same
   function within different enterprises are different.  In order for
   the intelligent agent to understand the execution results, it is
   necessary to standardize the output.  This requirement also exists
   for the input and output of small models and tool calls.

5.3.  Network Intent Recognition Requirement

   Network intent recognition refers to how to correctly understand the
   significance of various indicators for the network when receiving
   different data from the network, and how to analyze the collected
   indicators based on existing network operation and maintenance
   practices, extracted from human experience and knowledge manuals.  On
   the other hand, how human input is transformed into network goals and
   actions can also be a part of network intent recognition.  The
   current implementation.  This is where standardization is needed.




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   To support the implementation of closed-loop, a knowledge graph model
   can be constructed to inject large models.  After receiving user or
   network intent, perform entity recognition, retrieve knowledge graph,
   complete entity linking, and output structured intent.

   Graph Neural Network (GNN) refers to an algorithm that uses neural
   networks to learn graph structured data, extract and discover
   features and patterns from graph structured data, and meet the
   requirements of graph learning tasks such as clustering,
   classification, prediction, segmentation, and generation.  In the
   construction of network operation and maintenance knowledge, entities
   such as devices, links, and alarms can be abstracted into graph
   structures, and hidden relationships can be mined through message
   passing mechanisms to achieve the transformation of operation and
   maintenance problems from "passive response" to "active cognition".
   In the graph structure, physical devices can be nodes, connection
   relationships can be edges, and they can also be used to construct
   event correlation graphs.

5.4.  Self Close Loop Requirement

   Intelligent agents need to have the ability to complete tasks on
   their own without human intervention.  In addition to the
   aforementioned requirements for data input and intent understanding,
   the operation and maintenance agent should also have execution
   capabilities, including generating task plans, calling APIs, and
   completing actions.  This process involves issues such as permission
   management and security confidentiality.

5.5.  Resource Requirement

   The resource requirements for agent applications mainly include three
   aspects: storage, computing force, and network infrastructure.

   Storage should consider cloud edge coordination.  In the training and
   use of intelligent delivery, in addition to building a knowledge
   base, contextual information, user preferences, historical records,
   etc. also need to be continuously stored.  Some content may need to
   be stored on the user side, while others are more suitable for
   obtaining in the cloud.  Resource coordination and balance are key
   issues.  In addition, intelligent agents require a large amount of
   storage resources, and so much information requires efficient access
   solutions.

   The computing force part considers the unified allocation of
   computing resources, computation offloading, etc.





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   The network infrastructure mainly provides low latency and high
   bandwidth network support, which can use SRv6/G-SRv6 to compress
   packet headers, improve cross domain scheduling efficiency, and
   optimize long-distance data transmission throughput through RDMA.

   (Content to be expanded)

5.6.  Muti-Agent Collaboration Requirement

   In order to complete specific tasks, some can be accomplished through
   self-closed-loop of agents, while others require collaboration among
   multiple agents.  This includes three communication modes:
   interaction between humans and agents, interaction between agents and
   the environment, and interaction between different agents.  The
   interaction between humans and agents is the input of human
   intentions, usually in the form of natural language, and the input
   content is multimodal (voice, text, images, etc.).  The interaction
   between agents and the environment is usually achieved through
   interface access, which can obtain data, and triggering subsequent
   actions.  The interaction between different agents is the input and
   output interaction among agents, which involves both multimodal
   information exchange and system interface level interaction.

   In order to enable different agents to cooperate with each other,
   complete tasks together, and achieve a large closed loop, the
   following requirements are observed:

   *  Standardized communication protocol between agents.  Due to the
      need for communication between different agents, whose interfaces
      and output formats may vary, and even their communication content
      may differ, a unified communication format is required to ensure
      semantic consistency.

   *  Agent discovery.  Unlike traditional communication, where
      connections are established based on known addresses,
      communication through agent collaboration is temporary and the
      objects completing tasks are not unique, requiring self discovery
      to determine the communication objects.  To support this, the
      communication network needs to establish an intelligent agent
      discovery mechanism.

   *  Agent discovery.  Unlike traditional communication, where
      connections are established based on known addresses,
      communication through agent collaboration is temporary and the
      objects completing tasks are not unique, requiring self discovery
      to determine the communication objects.  To support this, the
      communication network needs to establish an intelligent agent
      discovery mechanism.



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   For the above three requirements, A2A, AGNTCY and others in the
   industry have proposed their own protocol implementations, but the
   complete solution that requires network adaptation involved in the
   above process is still blank.

   *  Unified Agnets connection control and arrangement.  Due to the
      frequent team communication and partner searching required between
      intelligent agents to complete tasks, different vendors may
      provide intelligent agents with similar functions.  It is best to
      have a unified intelligent agent management platform for easier
      team building.  Additionally, it can support network operations
      tasks that require orchestration.

   *  Impacts on the L5 layer Agent connections need to be managed
      separately to distinguish them from human autonomous behavior.

   *  Impacts on the L4 layer Meet networking requirements, etc.

   (Content to be expanded)

5.7.  Security Requirement

   (Content to be expanded)

6.  References

6.1.  Informative References

   [A2A]      Google, "Agent2Agent(A2A)
              Protocol：https://google.github.io/A2A/#/", April 2025.

   [LLMbasedAgents]
              Cheng, Y. Cheng., Zhang, C. Zhang., Zhang, Z. Zhang.,
              Meng, X. Meng., and S. Hong. Hong, "Exploring Large
              Language Model based Intelligent Agents: Definitions,
              Methods, and Prospects.", January 2024.

   [MCP]      Anthropic, "Model Context Protocol
              (MCP)：https://www.anthropic.com/news/model-context-
              protocol", November 2024.

6.2.  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/info/rfc2119>.




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Author's Address

   Chuyi Guo
   China Mobile
   Beijing
   100053
   China
   Email: guochuyi@chinamobile.com











































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