



Network Working Group                                            Q. Yuan
Internet-Draft                                                    J. Mao
Intended status: Standards Track                                  B. Liu
Expires: 5 May 2026                                              N. Geng
                                                                X. Shang
                                                                  Q. Gao
                                                                   Z. Li
                                                     Huawei Technologies
                                                         1 November 2025


         Use cases of the AI Network Traffic Optimization Agent
               draft-yuan-rtgwg-traffic-agent-usecase-00

Abstract

   This document introduces AI Network Traffic Optimization Agents as a
   dynamic alternative to traditional static network optimization
   methods.  These AI entities analyze real-time network status (e.g.,
   latency, node load) and adjust resources flexibly—deployed centrally
   or on devices—to enhance efficiency, ensure service quality, and cut
   operational costs.  It defines network traffic optimization
   (maximizing resource use, meeting QoS) and AI agents (autonomous,
   learning entities that reduce manual work), then details three key
   application scenarios: tunnel adjustment (adaptive routing,
   predictive bandwidth, fault recovery), traffic steering
   (classification, application-aware policies, pre-emptive load
   balancing), and network slice adjustment (lifecycle automation, SLA
   compliance, slice-specific fault fixes).  The document emphasizes the
   agents’ role in enabling SLA-compliant, autonomous optimization for
   complex networks.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   This Internet-Draft will expire on 5 May 2026.



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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
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Requirements Language . . . . . . . . . . . . . . . . . . . .   3
   3.  Network Traffic Optimization and AI Agent . . . . . . . . . .   3
     3.1.  Network Traffic Optimization  . . . . . . . . . . . . . .   4
     3.2.  AI Agent  . . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Usage Scenarios of the AI Network Traffic Optimization
           Agent . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Tunnel Adjustment . . . . . . . . . . . . . . . . . . . .   5
       4.1.1.  Adaptive Tunnel Routing . . . . . . . . . . . . . . .   5
       4.1.2.  Predictive Bandwidth Allocation . . . . . . . . . . .   5
       4.1.3.  Autonomous Fault Detection and Recovery . . . . . . .   6
     4.2.  Traffic Steering into Tunnels . . . . . . . . . . . . . .   6
       4.2.1.  Traffic Classification and Priority Mapping . . . . .   6
       4.2.2.  Application-Aware Steering Policies . . . . . . . . .   6
       4.2.3.  Pre-emptive Traffic Load Balancing  . . . . . . . . .   6
     4.3.  Network Slice Adjustment  . . . . . . . . . . . . . . . .   7
       4.3.1.  NSI Lifecycle Automation  . . . . . . . . . . . . . .   7
       4.3.2.  Closed-Loop SLA Compliance  . . . . . . . . . . . . .   7
       4.3.3.  Slice-Specific Fault Remediation  . . . . . . . . . .   7
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   8
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   8
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   8
   8.  Normative References  . . . . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8











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1.  Introduction

   AI Network Traffic Optimization Agents are intelligent entities that
   analyze real-time network telemetry (e.g., bandwidth occupancy,
   latency, node load, packet loss) and dynamically adjust network
   resources on behalf of operators.  Their core goals are to boost
   network efficiency, guarantee service quality, and lower operational
   costs for end-users.  These agents offer flexible deployment: they
   can be implemented on centralized network management platforms to
   integrate global data for holistic optimization, or embedded in edge
   devices (e.g., routers, switches, IoT gateways) to respond in real
   time to local traffic fluctuations.

   This deployment flexibility not only integrates intelligent decision-
   making with network infrastructure but also breaks through the rigid
   interaction barriers of traditional networks.  Unlike conventional
   network devices, which rely on strict, fully standardized protocols
   for communication—often plagued by version incompatibility and format
   constraints—AI agents enable adaptive, context-aware inter-device
   collaboration.  They natively support semi-structured data exchange
   and natural language interaction, allowing seamless communication
   across heterogeneous devices and reducing friction from fragmented
   protocols.

   Traditional network optimization relies heavily on static
   configuration rules and manual adjustments, limiting it to coarse-
   grained issue resolution.  During traffic surges (e.g., peak
   e-commerce sales, large-scale video conferences), this approach fails
   to adapt promptly, leading to increased latency or packet
   loss—especially detrimental to mission-critical applications
   requiring stable transmission.  In contrast, AI Network Traffic
   Optimization Agents enable fine-grained, autonomous optimization:
   they steer traffic to underutilized paths that meet application-
   specific SLA requirements, or allocate exclusive resource channels
   for high-priority services, ensuring performance remains unaffected
   by non-critical traffic.  Their interactive capabilities further
   amplify these advantages, enabling faster cross-device coordination
   and more agile response to dynamic network changes.

2.  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 RFC
   2119[RFC2119].

3.  Network Traffic Optimization and AI Agent




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3.1.  Network Traffic Optimization

   Network traffic optimization encompasses a suite of technologies,
   strategies, and practices focused on monitoring, managing, and
   dynamically adjusting data flows across a network.  Its core
   objectives are to maximize the efficiency of network resources (e.g.,
   bandwidth, node capacity), mitigate issues such as congestion,
   latency, and packet loss, and ensure critical applications (e.g.,
   online gaming, financial transactions, real-time video conferencing)
   meet their required Quality of Service (QoS) standards.

   By redistributing traffic to underutilized paths, prioritizing high-
   priority requests, and smoothing sudden traffic surges, it transforms
   passive network management into proactive adjustment.  This approach
   supports the stable operation of modern complex networks (including
   5G, edge computing, and multi-vendor hybrid environments) while
   minimizing unnecessary operational costs—with AI-driven interaction
   capabilities further enhancing its adaptability to heterogeneous
   network ecosystems.

3.2.  AI Agent

   An AI Agent is an automated intelligent entity designed to act on
   behalf of users, systems, or organizations to achieve specific goals.
   Its core capabilities include:

   *  Perceiving contextual information (e.g., real-time network status,
      user behavior, environmental changes) through multi-source data
      collection;

   *  Analyzing data via advanced algorithms (e.g., machine learning,
      reinforcement learning) to derive actionable insights;

   *  Making autonomous decisions and executing tasks independently or
      in collaboration with other agents;

   *  Supporting semi-structured data exchange (e.g., schema-less
      telemetry metrics, partial configuration snippets) to break free
      from rigid format constraints;

   *  Enabling natural language interaction (NLI) for simplified human-
      device and inter-device communication.

   Unlike traditional static programs, AI Agents can self-learn and
   iterate based on historical data, adapting to dynamic scenarios
   (e.g., real-time traffic path adjustment, personalized policy
   execution).  Their key value lies in reducing manual intervention,
   improving task efficiency, and addressing complex problems requiring



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   real-time, data-driven decision-making.  Critically, their flexible
   interaction models reduce reliance on strict standardization,
   minimizing version compatibility issues and enabling seamless
   integration of new devices—laying the foundation for rapid network
   iteration and scalability.

4.  Usage Scenarios of the AI Network Traffic Optimization Agent

   This section outlines typical application scenarios of AI Network
   Traffic Optimization Agents across three key network operation
   domains: tunnel adjustment, traffic steering into tunnels, and
   network slice adjustment.  Leveraging AI algorithms and real-time
   telemetry, the agents automate optimization, enhance service
   reliability, and ensure SLA compliance—while their interactive
   capabilities (semi-structured data support, natural language
   interaction) amplify efficiency and scalability.

4.1.  Tunnel Adjustment

   AI Network Traffic Optimization Agents optimize tunnels (e.g., RSVP-
   TE tunnels, SRv6 tunnels) by dynamically adapting to network
   conditions, ensuring efficient data transmission and fault
   resilience.  Their interactive capabilities streamline cross-device
   coordination, accelerating decision-making and recovery.

4.1.1.  Adaptive Tunnel Routing

   Agents collect real-time telemetry (e.g., link utilization, latency,
   packet loss) and network topology information (via protocols such as
   BGP-LS or IS-IS).  Using machine learning-based routing algorithms
   (e.g., reinforcement learning for path selection), they identify
   optimal tunnel paths.  When congestion or link degradation is
   detected, agents proactively recompute paths and share intent-driven
   instructions (via semi-structured data) with routers/switches to
   minimize end-to-end latency without relying on rigid protocol syntax.

4.1.2.  Predictive Bandwidth Allocation

   Agents analyze historical traffic patterns (e.g., diurnal peaks for
   enterprise services) to predict future bandwidth demands for each
   tunnel.  Through tunnel signaling protocols (e.g., RSVP-TE), they
   implement dynamic adjustment: reducing allocation during off-peak
   periods to avoid waste, and scaling up bandwidth preemptively before
   traffic surges.  Operators can also fine-tune prediction parameters
   via natural language prompts (e.g., “Increase bandwidth buffer for
   weekday 9 AM video conferences”), simplifying policy updates.





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4.1.3.  Autonomous Fault Detection and Recovery

   Agents monitor real-time tunnel KPIs (e.g., availability, jitter) and
   use anomaly detection models (e.g., autoencoders) to identify faults
   (e.g., link failures).  Upon detection, they automatically share
   fault details (via semi-structured data) with other agents and
   initiate recovery actions (e.g., switching traffic to pre-provisioned
   backups).  This cross-agent collaboration reduces Mean Time to
   Recovery (MTTR) by eliminating manual coordination delays.

4.2.  Traffic Steering into Tunnels

   AI Network Traffic Optimization Agents enable fine-grained traffic
   steering, mapping flows to tunnels that align with their QoS
   requirements.  Their support for multi-format data and natural
   language interaction simplifies policy configuration and cross-device
   coordination.

4.2.1.  Traffic Classification and Priority Mapping

   The agent can perform deep packet inspection (DPI) and flow analysis
   (via protocols such as NetFlow v9 or IPFIX) to classify traffic based
   on service type (e.g., VoIP, 4K video, bulk data), user priority
   (e.g., VIP users), and QoS class.Through policy-based routing (PBR)
   or segment routing policies, it maps classified traffic to tunnels
   with QoS capabilities that match the traffic’s needs—for example,
   low-latency tunnels for VoIP or high-bandwidth tunnels for bulk data.

4.2.2.  Application-Aware Steering Policies

   Agents use application signature recognition (e.g., TLS SNI, DNS
   queries) to identify application-specific traffic (e.g., Zoom, AWS S3
   transfers).  They enforce application-specific rules: real-time
   applications are directed to tunnels with guaranteed latency (<50ms)
   and low packet loss (<0.1%), while non-critical traffic uses cost-
   efficient tunnels.  Operators can define these rules via natural
   language (e.g., “Route all IoT sensor data to shared low-cost
   tunnels”), with agents translating prompts into executable
   policies—reducing configuration complexity.

4.2.3.  Pre-emptive Traffic Load Balancing

   Agents forecast traffic hotspots (e.g., regional surges from events)
   using time-series models (e.g., LSTM).  They implement pre-emptive
   steering to distribute predicted heavy traffic across parallel
   tunnels, preventing overload.  Agents share load distribution plans
   with edge devices via semi-structured data, ensuring uniform resource
   utilization across the network without strict protocol alignment.



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4.3.  Network Slice Adjustment

   AI Network Traffic Optimization Agents support the lifecycle
   management and optimization of Network Slice Instances (NSIs),
   focusing on resource efficiency, SLA compliance, and fault
   resilience.  Their flexible interaction models enable seamless
   collaboration between slice-specific agents, accelerating slice
   deployment and adjustment.

4.3.1.  NSI Lifecycle Automation

   Agents participate in NSI design by using slice requirements (e.g.,
   bandwidth, latency, isolation) to recommend optimal resource
   allocation (e.g., CPU, bandwidth, tunnel assignments) and topology
   configurations (e.g., dedicated vs. shared tunnels).  They automate
   instantiation and termination: during deployment, agents coordinate
   across devices to deploy required tunnels and steering rules (via
   semi-structured data exchange); upon termination, they release
   resources to prevent leakage.  This cross-agent collaboration reduces
   reliance on standardized interfaces, enabling faster slice
   deployment.

4.3.2.  Closed-Loop SLA Compliance

   Agents monitor slice-level KPIs (e.g., throughput, latency) in real
   time and compare them against SLA thresholds (e.g., 100 Mbps minimum
   throughput, 100ms maximum latency).  When SLA violations are
   predicted or detected, they trigger closed-loop adjustments (e.g.,
   augmenting tunnel bandwidth, optimizing routing paths).  Operators
   can also set SLA thresholds via natural language (e.g., “Ensure
   industrial IoT slice latency stays below 80ms”), making policy
   updates intuitive and agile.

4.3.3.  Slice-Specific Fault Remediation

   The agent can analyze multi-dimensional slice alarms (e.g., tunnel
   faults within the slice, resource shortages) via correlation models
   that integrate slice topology and historical fault data.  It enables
   slice-aware fault recovery: it identifies the root cause of slice
   degradation (e.g., a failed tunnel in the slice’s path) and executes
   slice-specific remediation (e.g., re-provisioning a dedicated backup
   tunnel for the slice), thereby minimizing impact on the slice’s
   services.








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5.  Conclusion

   This document systematically elaborates on AI Network Traffic
   Optimization Agents, covering their role in addressing traditional
   network limitations, core definitions of network traffic optimization
   and AI agents, and practical application scenarios.  Beyond dynamic
   resource allocation and SLA-compliant optimization, these agents
   deliver transformative value through enhanced inter-device
   interaction capabilities.

   By supporting semi-structured data exchange, AI agents break free
   from the rigid format constraints of traditional network protocols,
   enabling seamless communication across heterogeneous devices and
   vendors.  Natural language interaction simplifies policy
   configuration and human-device collaboration, lowering operational
   barriers.  These features reduce reliance on strict standardization
   and mitigate version compatibility issues, fostering network
   scalability and enabling rapid iteration of optimization strategies.

   In complex environments such as 5G, edge computing, and multi-vendor
   hybrid networks, AI Network Traffic Optimization Agents serve as a
   cornerstone of next-generation intelligent networks.  They not only
   automate fine-grained optimization to enhance efficiency and service
   quality but also through flexible interaction models, enable agile
   response to dynamic traffic patterns and emerging service
   requirements—future-proofing networks against technological evolution
   while minimizing operational costs.  As network ecosystems grow more
   complex, the interactive and adaptive capabilities of these AI agents
   will become increasingly critical to unlocking the full potential of
   intelligent network management.

6.  Security Considerations

   TBD.

7.  IANA Considerations

   TBD.

8.  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>.

Authors' Addresses




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   Quan Yuan
   Huawei Technologies
   Beijing
   100095
   China
   Email: yuanquan25@huawei.com


   Jianwei Mao
   Huawei Technologies
   Beijing
   100095
   China
   Email: maojianwei@huawei.com


   Bing Liu
   Huawei Technologies
   Beijing
   100095
   China
   Email: leo.liubing@huawei.com


   Nan Geng
   Huawei Technologies
   Beijing
   100095
   China
   Email: gengnan@huawei.com


   Xiaotong Shang
   Huawei Technologies
   Beijing
   100095
   China
   Email: shangxiaotong@huawei.com


   Qiangzhou Gao
   Huawei Technologies
   Beijing
   100095
   China
   Email: gaoqiangzhou@huawei.com





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   Zhenbin
   Huawei Technologies
   Beijing
   100095
   China
   Email: robinli314@163.com













































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