



Network Working Group                                              M. Yu
Internet-Draft                                                   A. Wang
Intended status: Informational                                     J. Li
Expires: 9 July 2026                                               Z. Li
                                                           China Telecom
                                                          5 January 2026


           AI Agent Use Cases and Requirements in 6G Network
                  draft-yu-ai-agent-use-cases-in-6g-02

Abstract

   This draft introduces use cases related to AI Agents in 6G networks,
   primarily referencing the technical report of 3GPP SA1 R20 Study on
   6G Use Cases and Service Requirements (TR 22.870).  It also
   elaborates on some of the requirements for introducing AI Agents into
   6G networks from the perspective of operators.

Status of This Memo

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   This Internet-Draft will expire on 9 July 2026.

Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.











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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Intent-based 6G Services Enabled by Network AI Agents . .   4
       2.1.1.  Use Case On 6G Network Providing On-demand Networking
               with AI Agent . . . . . . . . . . . . . . . . . . . .   4
       2.1.2.  Use Case On Intelligent Calling Services  . . . . . .   5
       2.1.3.  Use Case On Disaster Rescue Planning Enabled By Network
               AI Agents . . . . . . . . . . . . . . . . . . . . . .   5
       2.1.4.  Use Case On AI Agent For Network Performance
               Assurance . . . . . . . . . . . . . . . . . . . . . .   5
       2.1.5.  Use Case On Customized Service Provisioning Based On AI
               Agents  . . . . . . . . . . . . . . . . . . . . . . .   6
       2.1.6.  Use Case On Network-based Intelligent Assistance (e.g.
               for autonomous driving) By a Network-native AI Agent    6
       2.1.7.  Use Case On AI-optimized Smart Call Assistance For
               Telecom Networks  . . . . . . . . . . . . . . . . . .   7
     2.2.  Device-Network Collaboration  . . . . . . . . . . . . . .   7
       2.2.1.  Use Case On 6G System Assisted AI Agent Service . . .   7
       2.2.2.  Use Case On Smart Housekeeping  . . . . . . . . . . .   8
       2.2.3.  Use Case On Child Health Management Assistant . . . .   8
       2.2.4.  Use Case On Flexible UE-Network Coordination Through AI
               Agent(s)  . . . . . . . . . . . . . . . . . . . . . .   8
       2.2.5.  Use Case On Proactive AI Agent For Personal Safety  .   9
       2.2.6.  Use Case On Shared Embodied AI Agents . . . . . . . .   9
     2.3.  Multiple Devices Collaboration  . . . . . . . . . . . . .   9
       2.3.1.  Use Case On Collaborative AI Agents . . . . . . . . .  10
       2.3.2.  Use Case On AI Agents Communication . . . . . . . . .  10
       2.3.3.  Use Case On Authentication And Authorization For AI
               Agents  . . . . . . . . . . . . . . . . . . . . . . .  10
       2.3.4.  Use Case On Smart Support For Data Collection And
               Fusion In Multi-agent Scenarios . . . . . . . . . . .  11
     2.4.  Network-Application Collaboration . . . . . . . . . . . .  11
       2.4.1.  Use Case On Intelligent Communication Assistant . . .  11
       2.4.2.  Use Case On 6G AI Agents Collaboration With Third-party
               AI Using LLM  . . . . . . . . . . . . . . . . . . . .  11
       2.4.3.  Use Case On Network Knowledge As Part of Retrieval
               Augmented Generation For Generative AI  . . . . . . .  12



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       2.4.4.  Use Case On AI Agent Management . . . . . . . . . . .  12
   3.  Potential Requirements for 6G Network . . . . . . . . . . . .  12
     3.1.  The Identity of AI Agents . . . . . . . . . . . . . . . .  13
     3.2.  Efficient Collaboration . . . . . . . . . . . . . . . . .  13
     3.3.  Cross-Domain Collaboration  . . . . . . . . . . . . . . .  13
     3.4.  Registration and Discovery  . . . . . . . . . . . . . . .  13
     3.5.  Service and Data Exposure . . . . . . . . . . . . . . . .  13
     3.6.  Reliability Assurance . . . . . . . . . . . . . . . . . .  14
     3.7.  High-performance Communication  . . . . . . . . . . . . .  14
     3.8.  Security  . . . . . . . . . . . . . . . . . . . . . . . .  14
     3.9.  Energy Efficiency . . . . . . . . . . . . . . . . . . . .  14
   4.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  14
   5.  Informative References  . . . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   Currently, with breakthroughs in large language models and multimodal
   technologies, AI Agent has emerged as a major research focus in the
   industry.  Equipped with capabilities such as intent understanding,
   action planning, decision-making, task execution, and self-awareness,
   AI Agents can integrate environmental perception, memory, tool
   invocation, and multi-agent collaboration to accomplish complex
   tasks.  They have already demonstrated significant value in key
   fields like autonomous driving, intelligent customer service, and
   smart home systems.  In the 6G era, the introduction of AI Agent
   technology will enable operators to fully leverage the potential of
   mobile communication networks, significantly improving network
   operational efficiency and user experience.  As a result, AI Agents
   are expected to become a key research focus in future 6G networks,
   leading to deep integration between 6G and AI Agent technologies.

   In the 3GPP R20 standardization research for 6G, AI Agent has been
   one of the most discussed and debated topics, whether in SA1's study
   on 6G scenarios and requirements or SA2's research on network
   architecture.  In the SA1#109 meeting, 19 contributions related to AI
   Agents were submitted, which include 16 new use cases, with 4 use
   cases ultimately agreed.  And a preliminary definition of AI Agent
   from a capability perspective was adopted: "an automated intelligent
   entity capable of e.g interacting with its environment, acquiring
   contextual information, reasoning, self-learning, decision-making,
   executing tasks (autonomously or in collaboration with other Al
   Agents) to achieve a specific goal."  In the SA1#110 meeting, more
   than 30 contributions related to AI Agents were submitted, which
   include 22 new use cases, with 7 ultimately agreed.






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   This draft summarizes and categorizes the AI Agent-related use cases
   in 6G networks, with a brief introduction provided in Section 2.  In
   Section 3, from an operator's perspective, we elaborate on the
   potential requirements for introducing AI Agents into 6G networks,
   which should be considered when designing the agent communication
   related protocol in mobile communication network.  In Section 4, we
   conclude this draft.

2.  Use Cases

   AI Agents can be deployed at various locations within the 6G system.
   Depending on their deployment positions, AI Agents in 6G can be
   classified into On-device AI Agents (deployed on user devices),
   application AI Agents, network AI Agents (deployed within the future
   6G network), operation management AI Agents, etc.  For instance,
   terminal AI Agents refer to those implemented on end-user devices,
   while network AI Agents are those embedded within the 6G network.

   This section summarizes and categorizes AI Agent-related use cases in
   6G networks.  Unlike AI Agents in the internet domain, use cases
   involving AI Agents in mobile communication networks place greater
   emphasis on how network AI Agents can deliver 6G services to users,
   as well as how different AI Agents within the 6G system coordinate
   with each other.

2.1.  Intent-based 6G Services Enabled by Network AI Agents

   By deploying AI Agents within 6G network, the 6G network can provide
   users with intent-based services.  These intelligent services may
   represent combinations of multiple network capabilities, such as
   communication services, sensing services, AI/ML services, computing
   services, and more.  Users only need to express their intent to the
   6G network, without requiring specialized technical knowledge to
   decompose the intent into technical requirements.  In this context,
   3GPP SA1 has formally defined network intent as: Expectations
   including requirements, goals and constraints without specifying how
   to achieve them.

2.1.1.  Use Case On 6G Network Providing On-demand Networking with AI
        Agent

   User Harry owns a smart robot named Ron and has a lovely pet dog
   called Bob. Bob needs to be walked twice daily.  While away on a
   business trip, Harry sends his request through an operator portal
   (which could be an app, a mobile webpage, etc.) to the 6G network's
   AI Agent, expressing his intention for robot Ron to ensure Bob's
   safety during walks.  The network AI Agent processes this request,
   determines that the task requires perception services and QoS-



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   guaranteed services, and then distributes these services to the
   relevant network entities.

2.1.2.  Use Case On Intelligent Calling Services

   The network delivers AI Agents enabled intelligent calling services
   that revolutionize traditional voice communications.  By integrating
   recognition and perception capabilities of AI Agents, it offers two
   key functionalities: 24/7 Intelligent Answering (handling calls
   during unreachability, e.g., flight/power-off modes with contextual
   responses) and Intelligent Answering Machine (managing calls during
   user unavailability, e.g., meetings, with call logging).  These
   services operate under strict user authorization, allowing
   customization of voice tones, trigger conditions (e.g., flight mode
   activation), and data permissions (call records/summaries).  For
   instance, when a subscriber enables the service, the network
   autonomously answers calls based on predefined preferences and
   provides post-call analytics.

2.1.3.  Use Case On Disaster Rescue Planning Enabled By Network AI
        Agents

   When a disaster strikes, unpredictable challenges such as collapsed
   buildings, deformed roads, and communication outages make the rescue
   extremely complex.  By leveraging 6G network AI Agents for rescue
   planning, the rescue efficiency can be significantly improved,
   maximizing the protection of victims‘ lives and personal property.
   In this case, the intent may be “execute the rescue mission with
   multiple rescue robots in a certain area”. Upon receiving the intent,
   the network AI agents initiate the rescue planning and decompose the
   rescue into multiple operations and other standardized 3GPP service.
   This may specifically include: road obstacle sensing (sensing
   service), multi-robot rescue route planning (AI inference service),
   training obstacle avoidance models (AI training service), real-time
   optimal route computation for rescue robots (computing service) and
   communication resource allocation for disaster zones (communication
   service).

2.1.4.  Use Case On AI Agent For Network Performance Assurance

   AI agents are artificial entities that can perceive environments,
   make decisions, and act.  The AI Agents have evolved to LLM-based
   versions, leveraging LLMs’ strengths in knowledge acquisition,
   reasoning, and planning to decompose complex tasks into collaborative
   sub-tasks via perception, intent understanding, and plan reflection
   (with feedback and human interaction for robustness).  In 6G network,
   multi-agent systems address strict network demands of big events
   (e.g., national games with millions of participants over 15 days),



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   where Operator A deploys AI agents for performance assurance.  The
   workflow involves organizers submitting intent-based requirements
   (e.g., bandwidth, VIP service), AI agents decomposing tasks into
   network configuration, resource allocation, and real-time monitoring,
   service agents creating and refining action plans through reflection,
   and action agents executing via tools.  During the event, agents
   collaborate to ensure VIP QoE, monitor KPIs, and auto-adjust networks
   upon warnings.  This multi-agent collaboration fulfills 6G’s big-
   event needs while reducing labor, surpassing 5G’s limitations in
   real-time dynamic planning, frequent KPI collection, and plan
   reflection.

2.1.5.  Use Case On Customized Service Provisioning Based On AI Agents

   With telecom industries prioritizing personalized services, AI agents
   integrate with 6G network to boost efficiency and innovation.  This
   use case involves Bob (a 6G user), who needs high-quality 6G network
   support for a 2 pm online meeting during his tomorrow’s Beijing-
   Chengdu train trip (departing 9 am).  Assume that Operator A’s 6G-
   deployed AI agent enabling intent-based services, user-agent
   interaction, and third-party resource access via tool invocations.
   Bob sends his intent; the AI agent validates the intent, and fetches
   third-party data (e.g., train schedules) if needed, identifies
   possible routes and covering base stations, predicts meeting QoE, and
   pushes fee-included assurance packages.  After Bob’s selection, the
   agent pre-configures the network, ensuring his optimal meeting
   experience during the journey.

2.1.6.  Use Case On Network-based Intelligent Assistance (e.g. for
        autonomous driving) By a Network-native AI Agent

   The rapidly growing market for AI-driven traffic navigation/
   assistance (e.g., ADAS, autonomous vehicles) presents significant
   opportunities for 3GPP operators. 3GPP networks offer unique
   advantages: access to exclusive wide-area environmental/network data,
   distributed AI capabilities, low latency via edge computing, and the
   integration of communication-AI-sensing.  They provide three service
   categories: Category 1 (local inferencing with vehicle/network data,
   low cost), Category 2 (added network sensing, moderate cost), and
   Category 3 (external data integration, comprehensive assistance).
   Core components include the AI Toolbox (pre-trained models/
   algorithms), network-based intelligent assistant (AI Agent
   interpreting intents and orchestrating services), and UE-side
   Intelligent Assistance Application Entity.  The service flow involves
   UE registration, subscriber intent submission (e.g., safe
   navigation), AI Agent recommending customized services, subscriber
   selection, and real-time network service activation/monitoring to
   fulfill the intent (e.g., safe travel to destination).



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2.1.7.  Use Case On AI-optimized Smart Call Assistance For Telecom
        Networks

   A telecom operator integrates an AI-powered smart call assistance
   service into 6G network, leveraging in-network AI Agents to
   dynamically optimize voice/video call quality based on real-time
   network conditions, user intent, and historical data.  Assume that
   the network AI capabilities (e.g., AI Agents), UE (smartphones/VoIP
   devices) with AI for real-time call condition/QoE monitoring,
   privacy-compliant user data sharing, and pre-trained AI models are
   deployed.  The service flow starts with a user initiating a call; the
   UE’s AI monitors metrics like jitter and packet loss, requesting
   network adjustments if quality degrades.  The 6G AI Agent generates
   optimizations (e.g., codec adjustments, bandwidth allocation) and
   validates effects (e.g., via digital twin).  The UE provides QoE
   feedback, and the AI Agent continuously analyzes aggregated data,
   updating models if persistent issues (affecting single/multiple
   users) arise.

2.2.  Device-Network Collaboration

   With the rapid advancement of technologies like smartphones and
   lightweight large-scale AI models, capabilities of user devices have
   significantly expanded, enabling autonomous execution of certain AI
   tasks and independent decision-making.  However, due to inherent
   device limitations - including constrained computational resources
   and battery capacity - deploying complex AI agents or performing
   sophisticated AI tasks locally on devices remains challenging.
   Consequently, investigating optimal collaboration mechanisms between
   UE-based AI agents and network-based AI agents to accomplish complex
   tasks represents a critical research direction for 6G networks.

2.2.1.  Use Case On 6G System Assisted AI Agent Service

   AI-powered devices can interact with their environment—collecting
   data, making autonomous decisions, and executing actions.  The 6G
   system will enhance AI agents by providing supplementary
   environmental data (e.g., real-time sensing for traffic awareness)
   and dynamic QoS updates for adaptive decision-making.Additionally, 6G
   must support secure AI agent authentication and inter-agent
   communication, as traditional identifiers like SUPI/IMSI may not
   suffice for dynamic AI functionalities.  The rise of AI agents will
   also increase "horizontal traffic" between devices, enabling
   collaboration within agent groups and with third-party applications.







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2.2.2.  Use Case On Smart Housekeeping

   6G system could help to keep the family daily care and security,
   requiring advanced automation and management capabilities to maintain
   a comfortable and efficient living space.  There will be more AI
   related applications and intelligent devices (e.g. robots, UAVs,
   autonomous vehicles) in the 6G era.  Users will be able to express
   their requirements through natural language to convey their needs.
   In certain scenarios, multiple devices will need to collaborate to
   complete complex tasks.  The 6G system can dynamically coordinate
   devices based on user's supply and demand requirements.

2.2.3.  Use Case On Child Health Management Assistant

   Lily's smartwatch AI agent continuously tracks her vital signs (heart
   rate, body temperature) during school hours.  When detecting abnormal
   readings (elevated heart rate and temperature), the system
   automatically escalates monitoring frequency and initiates an
   emergency protocol by: (1) verifying authorization through the
   network, (2) selecting the optimal emergency contact (mother Emma,
   based on real-time proximity and availability data), and (3)
   coordinating with Emma's AI agent by sharing Lily's health metrics,
   location data, and environmental conditions.  The network facilitates
   this process by providing positioning services, environmental sensing
   data, and secure data transmission between authorized AI agents.
   Emma's AI agent then calculates the fastest route to Lily's location
   while receiving continuous health updates, enabling prompt medical
   intervention.  This scenario showcases the seamless integration of
   UE-based and network-based AI capabilities, including cross-domain
   data analysis, dynamic service invocation, and privacy-preserving
   emergency response mechanisms, ultimately delivering timely
   healthcare intervention while maintaining strict data security
   protocols.

2.2.4.  Use Case On Flexible UE-Network Coordination Through AI Agent(s)

   6G aims to support diverse terminals (cars, AR glasses, etc.) with
   advanced services beyond connectivity, but current service
   interaction faces fragmentation and reliance on user pre-knowledge of
   available services.  To address this, Operator O deploys AI agents in
   its 6G network for generic UE-network coordination.  When user A
   drives into city X, the service access AI Agent proactively
   recommends a regional sensing service to enhance driving safety,
   which A accepts—receiving beyond-line-of-sight sensing data.  After
   checking into a hotel, A’s connected AR glasses are notified of a
   regional computing service; with A’s permission, the AI agent
   coordinates application offloading/acceleration.  The AI agent
   dynamically adjusts: warning of potential downgrades in poor network



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   areas (advising local app execution) and providing communication
   quality maps/path recommendations in crowded spots, plus optional VIP
   QoS prioritization.

2.2.5.  Use Case On Proactive AI Agent For Personal Safety

   This use case presents a network-hosted personal safety AI agent in
   6G network, dedicated to proactively safeguarding users by
   integrating real-time data (location, wearable biometrics like heart
   rate/accelerometer, calendar) and environmental data (e.g., area
   crime statistics) to build user risk profiles.  Assume that Alex has
   subscribed to the service, granting explicit data access consent,
   configuring safety policies (emergency contacts, distress triggers),
   and 6G ensuring secure, low-latency agent hosting.  When Alex walks
   through an unfamiliar, high-crime area after dark, the agent monitors
   his data, detects a sudden spike in heart rate and sprinting, and
   activates a high-alert state.  It sends Alex a safety confirmation
   prompt and alerts his emergency contact Chloe.  Unresponsive after 30
   seconds, the agent auto-contacts emergency services with Alex’s real-
   time location and context.

2.2.6.  Use Case On Shared Embodied AI Agents

   A future shared embodied AI agent model will emerge, with entities
   like humanoid robots, robot dogs, and Automated Guided Vehicles
   (AGVs) deployed across cities for rental.  This boosts their
   utilization and makes AI tech more accessible, requiring 6G’s high-
   speed, low-latency network for real-time status reporting, location
   sharing, and interactions.  Assume that ShareRobot deploys such
   agents (with IDs, communication modules) registered to Operator A.
   Bob’s (AGV) Sam (registered to Operator B) can’t carry a mattress, so
   he rents a ShareRobot shared AGV via QR code—logging in, authorizing
   access to Sam, and binding them.  The two AGVs connect, share
   attributes, and collaborate to move the mattress.  Finally, Bob
   successfully transports the mattress, returns the shared AGV, pays
   for usage; ShareRobot pays Operator B for data traffic and related
   services.

2.3.  Multiple Devices Collaboration

   Under the powerful communication capabilities of 6G network, multiple
   on-device AI Agents can collaborate with each other to accomplish
   complex AI tasks.  These AI Agents may from either the same
   application or different applications.







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2.3.1.  Use Case On Collaborative AI Agents

   John and Ann's electric vehicle (EV) uses an AI Agent to optimize
   charging based on dynamic energy prices and travel plans.  While John
   sleeps during a business trip, his EV's AI Agent detects high
   electricity prices at the hotel location and considers selling
   battery power back to the grid.  To verify feasibility, it securely
   accesses both John and Ann's calendar AI Agents (hosted by different
   providers) without waking them.  Learning of John's planned 900km
   return trip, the AI Agent cancels the energy sale.  All cross-border
   data exchanges maintain strict privacy, blocking unauthorized access
   (e.g., from friends' AI Agents).  This demonstrates how standardized
   AI Agent interoperability enables intelligent, user-authorized
   decisions across distributed systems.

2.3.2.  Use Case On AI Agents Communication

   A group could be established for users and their AI agents to
   communicate with each other.  To complete a complex task involving
   multiple users and triggered by a user, AI agent or application,
   communication domain for multiple groups could be established,
   Communication domain could be dynamically created for users and AI
   agents from multiple groups to communicate with each other for a
   specific task during a specific time.  Only the AI agents in the same
   domain can communicate with each other.  If authenticated /
   authorized, users and AI agents could join this group via various
   access technologies, including the cellular network, WiFi and
   Ethernet, etc.

2.3.3.  Use Case On Authentication And Authorization For AI Agents

   The security risks (malicious intent, intent misinterpretation) of AI
   Agents are critical.  Thus, authentication (verifying AI agent/user
   identity) and authorization (limiting access to subscribed services)
   are essential, with distinct policies for UEs and on-device AI
   agents.  A case in point: an invite-only AR exhibition, where
   authorized AI agents in smart glasses enable personalized AR content
   via the operator’s ultra-low-latency, high-bandwidth network.  Alice
   and Bob (invited, registered) and Cindy (impersonating Dale via his
   glasses) launched the AR app.  The network authenticated all AI
   agents but failed Cindy’s user authentication; only Alice and Bob got
   approved, accessing dedicated data paths for real-time AR rendering.
   Cindy was rejected, and the network mitigated threats through dual
   authentication/authorization.







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2.3.4.  Use Case On Smart Support For Data Collection And Fusion In
        Multi-agent Scenarios

   This use case describes a smart collaboration scenario where several
   robots (UEs) with data collection and processing capabilities and
   direct/indirect network access collaboratively build an information
   set via data/sensor fusion.  Emphasizing energy, resource efficiency,
   and situation-aware communication, the robots generate diverse,
   dynamically changing AI traffic with varying QoS requirements.  They
   share real-time traffic demands with the 6G network and a fusion
   center; an AS (trusted third party) centrally coordinates, e.g.,
   instructing pre-processing or task splitting.  The 6G network adapts
   to dynamic traffic changes (e.g., robot/object distance) to ensure
   reliable communication.

2.4.  Network-Application Collaboration

   The 6G network AI Agents and application AI Agents can fully
   collaborate to accomplish network tasks.  On one hand, AI agents
   within the 6G network can invoke appropriate application AI Agents
   based on service characteristics.  On the other hand, the network AI
   Agents can share network data and domain expertise with application
   AI Agents, providing crucial data support for application AI Agents.

2.4.1.  Use Case On Intelligent Communication Assistant

   Currently, most of the personal AI assistants are provided on the
   devices (e.g. smart phones).  However, the limitation of the power
   and thermal factors are the bottlenecks of the AI assistant
   development on devices.  Operators are highly possible to provide the
   Intelligent Communication Assistant services leveraging 6G network AI
   Agents.  For example, Alice is a business traveler, and her personal
   assistant in 6G network automatically monitors flight status, books a
   taxi upon landing by interfacing with the taxi company's registered
   AI service, and guides her to the vehicle using real-time location
   data - all without taxing her smartphone's resources.  This includes
   collaboration with AI Agents for applications such as taxi booking
   and real-time navigation.

2.4.2.  Use Case On 6G AI Agents Collaboration With Third-party AI Using
        LLM

   A 3rd party application (e.g. a smart city traffic management system)
   AI Agent sends a text-based request or query to the 6G network.  The
   request is processed by an AI agent in the 6G network that leverages
   LLMs and the network's advanced capabilities (e.g.  sensing, real-
   time data processing, telemetry, analytics, and others) to provide a
   response or perform an action.  The 6G network AI agent acts as an



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   intelligent intermediary, interpreting the text-based request,
   gathering necessary data, and returning a response or executing a
   task.

2.4.3.  Use Case On Network Knowledge As Part of Retrieval Augmented
        Generation For Generative AI

   Generative AI (including LLMs and multi-modal models) combined with
   Retrieval Augmented Generation (RAG)—which retrieves external
   knowledge to augment prompts before generation—enhances output
   quality with up-to-date information while reducing model retraining
   energy costs.  In 6G, MNOs deploy diverse network knowledge sources
   (static/dynamic data like roaming conditions, coverage, performance
   predictions) to support RAG-powered services such as XR city
   sightseeing.  Subscribed user Alice invoke XR apps, prompting
   Generative AI to use RAG for accessing relevant network knowledge.
   The app selects suitable knowledge sources, retrieves data, and
   generates optimized outputs (e.g., XR previews adapting to roaming/
   coverage constraints).  Benefits include improved user experience,
   energy savings, and digital inclusion, though retrieval may introduce
   delays.  Existing 5G lacks full RAG support, making 6G’s timely
   multi-source knowledge provision critical.

2.4.4.  Use Case On AI Agent Management

   To address global elderly care challenges amid aging populations, 6G
   network enable cross-ecosystem collaboration of third-party AI agents
   on smart devices (e.g., cameras, bracelets, TVs) via operator-managed
   registration and invocation mechanisms.  Operator A provides AI agent
   management for 70-year-old Mary, whose devices register capabilities
   (fall detection, heartbeat monitoring, video call) to the 6G network.
   When Mary’s smart camera detects a fall, it triggers emergency
   services directly or via the network.  The emergency center requests
   real-time data; the 6G network invokes her bracelet’s AI agent for
   heartbeat monitoring and the camera’s agent for live video (with
   consent).  It further activates the TV’s AI agent for a video
   call—featuring volume amplification, dialect translation, and
   instructional videos—to guide Mary.  Finally, Mary handles injuries
   correctly while awaiting paramedics.  Existing 5G features partially
   support this, but 6G’s cross-ecosystem coordination are critical.

3.  Potential Requirements for 6G Network

   In this section, we present potential requirements to 6G network that
   may arise from the introduction of AI Agents in 6G mobile
   communication network from an operator's perspective.  Some of these
   potential requirements have already been agreed by 3GPP, while others
   have not yet been adopted by 3GPP.



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3.1.  The Identity of AI Agents

   The 6G network shall support secure authentication, authorization,
   and management mechanisms for AI Agents' digital identities.  These
   AI Agents include on-device AI Agents, 3rd party AI Agents, network
   AI Agents, etc.  A robust identity management mechanism is the
   prerequisite for interactions between users and AI Agents, as well as
   between different AI Agents.

3.2.  Efficient Collaboration

   The 6G network shall support efficient collaboration between
   different AI Agents and between AI Agents and the tools.  This
   include: developing agent communication protocols better suited for
   6G network characteristics, supporting multimodal data (such as text,
   audio, video, etc.) interactions, enabling rapid transmission of
   massive data volumes, etc.

3.3.  Cross-Domain Collaboration

   Future AI agents will be ubiquitous, forming a device-network-
   industry end-to-end ecosystem. 6G network shall support the cross-
   domain collaboration of AI agents, including the device domain, RAN
   domain, core network domain, operation and management domain,
   application domain, etc.

3.4.  Registration and Discovery

   The 6G network shall support mechanisms for on-device AI Agents, 3rd
   party AI Agent, network AI Agents and tools to register their
   attributes to 6G network, which enables efficient, cross-platforms
   and cross-domain AI Agents and tools discovery.  This may different
   from the discovery mechanism in existing agent communication related
   protocol (e.g. NRF discovery mechanism).

3.5.  Service and Data Exposure

   The 6G network shall support secure mechanisms to expose the 6G
   services (e.g. sensing service, computing service, AI/ML service,
   etc.) and network data (e.g. sensing data, positioning data, etc.) to
   3rd party AI Agents.










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3.6.  Reliability Assurance

   The 6G network shall be able to provide mechanisms (e.g. network
   digital twin) to ensure the reliability and the validity of the
   decisions made by the AI Agents.  The decisions made by the AI Agents
   in 6G network may directly change the network status, parameters,
   configurations.  Only decisions that have been verified for
   reliability can be executed to change the network environment.

3.7.  High-performance Communication

   The 6G network shall enable high-performance communication, which may
   include low latency, high band-width, ultra-high data rate, etc.
   This is crucial for numerous scenarios such as device-network
   collaboration, network-application collaboration.

3.8.  Security

   The security of AI Agents communication in 6G includes the data
   protection and user consent.  Data pravacy means tha 6G network shall
   support end-to-end encryption for the interactions between AI Agents
   to ensure robust data protection and privacy security for sensitive
   information.  Besides, 6G network shall be able to provide mechanisms
   to collect the user consent for the local data collection.

3.9.  Energy Efficiency

   The 6G network shall be able to provide mechanisms to optimize the
   communication between AI Agents (especially for the on-device AI
   Agents) to reduce energy consumption.

4.  Conclusion

   AI Agents are expected to represent a critical innovation vector for
   6G.  This draft explores the transformative potential of AI Agents in
   6G network, outlining key use cases and operational requirements from
   an operator’s perspective.  When designing agent communication
   related protocols for 6G network, the aforementioned requirements
   should be thoroughly considered and incorporated into the protocol
   architecture.

5.  Informative References

   [TR_22.870]
              "3GPP TR 22.870, "Study on 6G Use Cases and Service
              Requirements", 2025.".





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Authors' Addresses

   Menghan Yu
   China Telecom
   Beiqijia Town, Changping District
   Beijing
   Beijing, 102209
   China
   Email: yumh1@chinatelecom.cn


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


   Jinyan Li
   China Telecom
   Beiqijia Town, Changping District
   Beijing
   Beijing, 102209
   China
   Email: lijinyan@chinatelecom.cn


   Zhen Li
   China Telecom
   Beiqijia Town, Changping District
   Beijing
   Beijing, 102209
   China
   Email: liz779@chinatelecom.cn















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