



Network Working Group                                        M. Han, Ed.
Internet-Draft                                             N. Zhang, Ed.
Intended status: Informative                              X. Gu, Ed.
Expires: 8 January 2026                                     China Unicom
                                                             7 July 2025


            The Impact of AI Agent to Network Infrastructure
                   draft-han-ai-agent-impact-infra-00

Abstract

   This document disucss and analyses the impact of AI Agent on network
   infrastructure aiming to facilitate the discussion and
   standardization about AI Agent communication within IETF.

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   This Internet-Draft will expire on 8 January 2026.

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   Copyright (c) 2025 IETF Trust and the persons identified as the
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   provided without warranty as described in the Revised BSD License.





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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions and Definitions . . . . . . . . . . . . . . . . .   3
   3.  Network for Agent Communication . . . . . . . . . . . . . . .   3
     3.1.  Scenarios . . . . . . . . . . . . . . . . . . . . . . . .   3
       3.1.1.  Agent Communication in LAN  . . . . . . . . . . . . .   3
       3.1.2.  Agent Communication across WAN  . . . . . . . . . . .   4
       3.1.3.  Agent Communication in Fields . . . . . . . . . . . .   4
     3.2.  Gap Analysis  . . . . . . . . . . . . . . . . . . . . . .   5
       3.2.1.  Agent ID management among heterougenous networks  . .   5
       3.2.2.  Mobility Management (esp. for Non-3GPP networks)  . .   5
       3.2.3.  Pervassive Point-to-Point Secure Channel  . . . . . .   5
     3.3.  Potential new works . . . . . . . . . . . . . . . . . . .   6
       3.3.1.  Pervasive Mobility Managment in fixed networks  . . .   6
       3.3.2.  On-demand Point-to-Point Private Line service . . . .   6
   4.  Agent-based Network O&M . . . . . . . . . . . . . . . . . . .   6
     4.1.  Scenarios . . . . . . . . . . . . . . . . . . . . . . . .   6
       4.1.1.  Autonomic O&M through AI Agent built into Devices . .   6
       4.1.2.  Agent interactions between Devices and Controllers  .   6
     4.2.  Potential new works . . . . . . . . . . . . . . . . . . .   7
       4.2.1.  Extension of Anima Framework & Protocols  . . . . . .   7
       4.2.2.  South-bound Interface Extension for Agentic O&M . . .   7
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   7
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   The rapid advancement of AI agents has introduced transformative
   changes across various domains, particularly in network
   infrastructure.  AI agents, the autonomous, intelligent entities
   capable of perception, reasoning, and decision-making, are
   increasingly being deployed in diverse scenarios.  On the one hand,
   the network needs to provide communication support for AI agent
   collaboration, such as industrial, office, smart home and other
   scenarios.  On the other hand, AI agents can be deployed in the
   network to achieve automatic network O&M.

   Traditional network infrastructures were primarily designed for
   human-driven communication (e.g., web browsing, video streaming).
   However, the rise of multi-agent systems, where AI agents collaborate
   or compete in real-time, demands ultra-low-delay communication,
   dynamic resource allocation, and enhanced security protocols.




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   Besides, AI agents are revolutionizing network O&M.  AI agents,
   powered by reinforcement learning and large language models, enable
   autonomous fault detection, predictive maintenance, and intent-driven
   networking.

   This draft explores the impact of AI Agents on network
   infrastructure, with a focus on Network for Agent Communication and
   Agent-based Network O&M.

2.  Conventions and Definitions

   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.  Abbreviations and definitions used in this
   document:

3.  Network for Agent Communication

3.1.  Scenarios

3.1.1.  Agent Communication in LAN

   a.  Office networks In the office network scenario, agents rely on a
   stable LAN environment to build an efficient collaboration system.
   Typical office agents, such as personal AI assistant, can
   automatically analyze users' work requirements, such as writing
   emails, organizing reports, and quickly generating drafts.  In the
   situation of team collaboration, multiple agents share data in real-
   time, automatically arrange task priorities, which can reduce manual
   communication costs and significantly improve work efficiency.  There
   is a requirement for office network to provide security guarantees
   for data interaction among agents to prevent the leakage of sensitive
   information.

   b.  Industrial networks In the industrial scenario, the LAN
   establishes a low-delay and highly reliable communication environment
   for agents, enabling efficient interconnection among production
   equipment, logistics robots, and management systems.  For example, in
   the warehousing and logistics process, the agents carried by AGV
   communicate with the central dispatching agent through the network,
   dynamically planning transportation routes based on real-time traffic
   conditions to improve the efficiency of material distribution.







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3.1.2.  Agent Communication across WAN

   a.  Agents directly access to public networks When agents directly
   access public network, they can achieve efficient coordination over a
   wide range and across regions.  For example, in smart city, traffic
   management agents, environment monitoring agents, and public service
   agents collaborate efficiently through public networks.  However, the
   openness of public networks also brings challenges in data security
   and privacy protection, which require the use of technologies such as
   encryption and authentication to ensure the security of agent
   communication.

   b.  Agents in Campus/DC to communicate across the WAN AI agents
   within different DCs can achieve resource sharing and remote
   collaboration through WAN.  For example, when a data center
   experiences a shortage of computing resources, the resource
   scheduling agent in this DC negotiates with resource scheduling
   agents of other DCs through the WAN to divert some tasks to DCs with
   idle resources.  This requires the WAN to ensure low delay.

3.1.3.  Agent Communication in Fields

   a.  Interaction through 3GPP radio networks In the office network
   scenario, agents rely on a stable LAN environment to build an
   efficient collaboration system.  Typical office agents, such as
   personal AI assistant, can automatically analyze users' work
   requirements, such as writing emails, organizing reports, and quickly
   generating drafts.  In the situation of team collaboration, multiple
   agents share data in real-time, automatically arrange task
   priorities, which can reduce manual communication costs and
   significantly improve work efficiency.  There is a requirement for
   office network to provide security guarantees for data interaction
   among agents to prevent the leakage of sensitive information.

   b.  Interaction through WiFi In the industrial scenario, the LAN
   establishes a low-delay and highly reliable communication environment
   for agents, enabling efficient interconnection among production
   equipment, logistics robots, and management systems.  For example, in
   the warehousing and logistics process, the agents carried by AGV
   communicate with the central dispatching agent through the network,
   dynamically planning transportation routes based on real-time traffic
   conditions to improve the efficiency of material distribution.

   c.  Interaction through satellite networks In the industrial
   scenario, the LAN establishes a low-delay and highly reliable
   communication environment for agents, enabling efficient
   interconnection among production equipment, logistics robots, and
   management systems.  For example, in the warehousing and logistics



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   process, the agents carried by AGV communicate with the central
   dispatching agent through the network, dynamically planning
   transportation routes based on real-time traffic conditions to
   improve the efficiency of material distribution.

3.2.  Gap Analysis

3.2.1.  Agent ID management among heterougenous networks

   Agent ID management in heterogeneous networks refers to the processes
   and systems for uniquely identifying, authenticating, and managing
   agents (software entities, devices, or services) across networks with
   different architectures, protocols, and technologies.  Key challenges
   include interoperability, scalability, security, dynamicity, and
   privacy protection.

3.2.2.  Mobility Management (esp. for Non-3GPP networks)

   The AI agent era exacerbates existing gaps in mobility management,
   particularly in interoperability, security, and context awareness.
   Addressing these requires,

   AI-native mobility frameworks that prioritize agent intent over
   device-centric logic.  Dynamic, secure abstraction layers for cross-
   protocol interoperability.  Standardized interfaces for agents to
   share mobility intent with networks.  Energy-efficient, role-based
   QoS models tailored to autonomous AI agents.

3.2.3.  Pervassive Point-to-Point Secure Channel

   Pervasive P2P secure channels in the AI agent era require a paradigm
   shift from device-centric, static security to agent-aware, dynamic,
   and scalable frameworks.  Addressing these requires,

   Developing lightweight, context-aware trust models for ephemeral
   agent interactions.  Implementing distributed key management and low-
   latency protocols for massive agent swarms.  Creating adaptive
   security abstractions that work across heterogeneous networks.
   Integrating privacy-preserving techniques, such as metadata
   obfuscation and fine-grained access control, into core channel
   design.  Establishing AI-specific standards that align security with
   agent roles and autonomy.









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3.3.  Potential new works

3.3.1.  Pervasive Mobility Managment in fixed networks

   TBD

3.3.2.  On-demand Point-to-Point Private Line service

   TBD

4.  Agent-based Network O&M

4.1.  Scenarios

4.1.1.  Autonomic O&M through AI Agent built into Devices

   In traditional congestion control method, there is a lack of cross-
   device historical data sharing and AI model collaboration between
   devices.  It is unable to adaptively optimize based on real-time
   traffic patterns.  When AI agents are introduced into network
   devices, intelligent collaboration can be achieved.  Devices
   synchronize real-time link bandwidth and TOP-N traffic
   characteristics through BGP extension.  The AI agent dynamically
   define congestion thresholds based on traffic data, replacing manual
   threshold configuration.  Upon detecting congestion, devices
   negotiate AI-generated policies (e.g., dynamic adjustment of Multi-
   Exit Discriminator (MED) values) and route traffic precisely to
   lightly loaded links.  Reinforcement learning is applied to
   dynamically optimize policy parameters during this process.

4.1.2.  Agent interactions between Devices and Controllers

   In the current IPv6 end-to-end traffic monitoring scenario, traffic
   data collection and analysis rely on manual intervention, while the
   large volume of live network traffic data results in high resource
   requirements.  When AI agents are deployed in network controllers and
   devices, intelligent collaboration can be achieved between
   controllers and edge devices.  The controller agent collects and
   monitors IPv6/IP traffic data in real time, and performs preliminary
   analysis including flow pattern recognition and IPv6/IPv4 traffic
   ratio trending.  The device agent collects customer traffic data,
   decomposes traffic distribution characteristics to identify high-
   value business scenarios, and synchronizes these insights to the
   controller agwnt.  The controller agent integrates global traffic
   ingress/egress data to construct regional traffic matrices, obtaining
   analysis results such as traffic distribution and link utilization.





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4.2.  Potential new works

4.2.1.  Extension of Anima Framework & Protocols

   Considering the Anima Framework & Protocols [RFC7575] with AI Agent,
   which provides more intelligent operation and management of network
   devices to achieve the Intention-driven network and Auto-driven
   network.

4.2.2.  South-bound Interface Extension for Agentic O&M

   The relatted agent communication protocols such as MCP, A2A etc.,
   need to be extended to support the collabration between network
   devices and controller.

5.  Security Considerations

   TBD

6.  IANA Considerations

   TBD

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/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>.

7.2.  Informative References

   [RFC7575]  Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
              Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
              Networking: Definitions and Design Goals", RFC 7575,
              DOI 10.17487/RFC7575, June 2015,
              <https://www.rfc-editor.org/info/rfc7575>.

Authors' Addresses






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   Mengyao Han (editor)
   China Unicom
   Beijing
   China
   Email: hanmy12@chinaunicom.cn


   Naihan Zhang (editor)
   China Unicom
   Beijing
   China
   Email: zhangnh12@chinaunicom.cn


   Xinrui Gu (editor)
   China Unicom
   Beijing
   China
   Email: guxr12@chinaunicom.cn
































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