



CATS Working Group                                              T. Jiang
Internet-Draft                                                    P. Liu
Intended status: Informational                              China Mobile
Expires: 1 January 2026                                     30 June 2025


        CATS Reference Model for AI-Agent Communication Network
                   draft-jiang-cats-reference-acn-00

Abstract

   This draft describes the AI-agents along with the network to provide
   the communication services among various types of AI-agents, i.e.,
   AI-agent Communication Network or ACN.  Thanks to the CATS-like
   information flow steering in ACN, we propose a CATS reference model
   that covers the definition of reference points, protocol stacks,
   service provisioning model, sigaling procedures, message paths, and
   implementation schemes.  This reference model is generalized so as to
   accommodate both the existing CATS framework and the potential
   extension for the ACN.

Status of This Memo

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   This Internet-Draft will expire on 1 January 2026.

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   extracted from this document must include Revised BSD License text as
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  AI-agent Communication Network (ACN)  . . . . . . . . . .   2
     1.2.  ACN Realization Models  . . . . . . . . . . . . . . . . .   3
   2.  Applications of CATS to ACN . . . . . . . . . . . . . . . . .   4
     2.1.  AI-agents Services with CATS-like Optimization  . . . . .   4
     2.2.  CATS-like Metrics Model for ACN . . . . . . . . . . . . .   5
   3.  CATS Reference Model for ACN  . . . . . . . . . . . . . . . .   6
     3.1.  CATS Reference Model and Reference Points . . . . . . . .   9
     3.2.  Examples of CATS Reference Points . . . . . . . . . . . .  10
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   6.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     6.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
     6.2.  Informative References  . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   AI agents are software-driven entities with embedded artificial
   intelligence, including machine learning and natural language
   processing, to interact multi-modally with applications, end devices
   and network components.  AI agents may exist either in the physical
   state as embedded HW devices (e.g., robots) or in the virtual state
   (e.g.,software-implmented applications).  With the integration of
   LLMs, AI agents can understand complex requests, translate them into
   actionable insights, and orchestrate various services (e.g.
   communication service, data analyzing service, AI-related services).
   AI agents play a cruical role in the Telecom domain by enhancing the
   network efficiency via dynamically optimizing resources, predicting
   network conditions, making autonomous & intelligent decisions, and
   facilitating seamless communication among serviced & servicing
   entities [AI-Agent-6G-ARC][TR.22.870].

1.1.  AI-agent Communication Network (ACN)

   With the imminent full unfolding of 6G era, the future world is
   expected to be full of AI agents, bearing versatile morphism and
   different capabilities.  In light of the seeming differentiation
   between the AI-agent centric and the human-object oriented
   communication modes, e.g., flow interactions, requirements of
   capability exposure, and trust control & management models, etc., it
   is highly imperative to define a new network framework that is



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   tailored to advance the communication among AI-agents, while
   simultaneously satiating the requirements of existing network
   entities.

   This new network framework is defined as the AI-agent Communication
   Network or ACN.  ACN targets at architecting a globally
   interconnected network to satisfy the on-demand communication,
   interactions & collaborations with secure and controllable
   information flow paths for AI-agents in distributed deployment mode,
   regardless of their instantiation formats (i.e., being in physical or
   virtual state), capability disparities (being in high-, mid- or low-
   rank), and/or the hosting devices [CMCC-ACN-WP].

   Commonly integrated with LLMs, AI-agents demand high computing power
   and significant energy consumption, which deems the versatility of
   the specific realization forms of AI agents.  For example, an AI-
   agent can be instantiated as a standalone physical body, or as an
   intelligent service (in software state) deployed inside the hosting
   network, or as a cloud-native instance residing in the edge or remote
   cloud data centers (DCs), or even as hybrid composite entity
   integrating all the advantages of physical body, hosting network and
   cloudified deployment.

1.2.  ACN Realization Models

   The versatile forms of the AI-agent realization may result in three
   typical architecture and communication models for ACN, namely:

   1.  Static intra-domain only AI-agent Communication: A network
       architectural model for the communication among AI-agents
       residing within a single administrative domain or network.  AI-
       agents in the domain form a network group maintaining the static
       communication association.  AI-agents inside the domain do not
       communicate with AI-agents outside the domain.

   2.  Static inter-domain AI-agent Communication: A network
       architectural model for the communication among AI-agents that
       reside across multiple administrative domains.  AI-agents across
       these domains form one or more network groups maintaining the
       static communication associations.  Note that in this model, AI-
       agents in a domain can communicate with AI-agents both inside and
       outside the domain.

   3.  Dynamic multi-domain AI-agent Communication: A network
       architectural model for the communication among AI-agents that
       may dynamically form a network group to handle a temporarily-
       generated task.  These AI-agents could be in the same or
       different administrative domains.  Once the temporary task is



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       finished, the dynamically-formed network gorup would be released
       and the communication session(s) among the involved AI-agents are
       terminated.  This is a dynamic communication mode versus the
       previous two static modes.

2.  Applications of CATS to ACN

   As stated in the Section 1.1, an AI-agents may manifest in different
   instantiation forms, e.g., embedded in a physical body, as an
   (software) APP service, as a cloud-native instance, or even as a
   hybrid composite entity.  These variations imply AI-agents own
   different capabilities, functional objectives, resource
   optimizations, etc.  Sometimes, the limitations of AI-agents, either
   those provisioning a service or those realizing a service, may lead
   AI-agents in an ACN to pursue the service optimization with the
   principles that are commensurate with CATS's objectives
   [CATS-PS-UseCase-Req].

2.1.  AI-agents Services with CATS-like Optimization

   AI-agents in an ACN demand normally intensive compute power and
   accordingly heavy energy consumptions.  However, the capabilities
   (either statically provisioned resources or dynamic runtime loads)
   among AI-agents, the AI related demands, and the data processing
   tasks varies dramatically.  For example, the compute power of
   lightweight terminals, such as smartphones, XR glasses, etc., is
   difficult to handle locally the complex computation tasks with more
   than billions of parameters.  In contrast, if all the complex tasks
   are delegated to more advanced cloudified instances (in either SW or
   HW format residing in a remote data center), then it might impair the
   real-time responsiveness if the remote instances experience the
   bursty load of multi-users.

   Therefore, it is more desirable to consider the seamless
   collaborations among end devices, networks and (edge or remote)
   clouds to build a composite AI-agent communication network, in which
   AI logics are distributed either at the network edge or within the
   network, either inside or across domains.  In that, the compute power
   at end devices may realize hierarchical AI reasoning with the help of
   more powerful network entities, which expands the end-side AI
   services on demand.  Further, the network can also provide more
   advanced AI services, e.g., Ambient IoT [TS.23.369], integrated
   sensing [TR.23.700-14], etc., to supplement the requirements of (end)
   AI-agents and lower the compute demands in them.  The ultimate goal
   would be the better balance between achieving the intelligence at AI-
   agents and the lower energy consumption (of course, potentially more
   advantages).  This certainly conforms to what CATS is promoting.




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   The Section 1.2 exemplifies three different ACN realization models,
   i.e., the static intra-domain, the static inter-domain, and the
   dynamic multi-domain.  AI-agents offering varied types of services
   could reside in any domain in a (multi-domain) ACN.  Supposing a
   complex task is comprised of multiple sub-tasks that need to be
   handled in a sequence, and every sub-task may be potentially serviced
   by more than one AI-agent.  If these AI-agents are distributed across
   different domains of an ACN, the service-chaining formed from the
   (across-domain) AI-agents makes the task processing more challenging.
   In this scenario, the application of CATS principles to the selection
   of the optimal AI-agent (among all candidates) for each sub-task may
   help fulfill the complex task more efficiently.

2.2.  CATS-like Metrics Model for ACN

   The CATS IETF draft on metrics definition [CATS-Metrics-Definition]
   specifies two types of metrics, namely the traditional network
   metrics that focus on the network resources and dynamic runtime
   information, and the compute metrics that describes the functional
   capabilities, resource consumption, system performance, etc., for
   service instances which would normally reside in edge or remote DCs.

   *  Network metrics: For network entities like routers or switches,
      they can be bandwidth, capacity, throughput, transmission delay,
      TX bytes, RX bytes, host bus utilization, etc.

   *  Compute metrics: For compute nodes, end servers, and/or service
      instances, they can be CPU, GPU, NPU, memory, storage, system
      delay.

   When the similar metrics model is extended to the AI-agent
   Communication Network or ACN, there would be a new type of metrics,
   defined as the 'AI-agent metrics' in this draft, to specify the
   unique characteristics of AI-agents.  These metrics could consist of:

   *  AI-agent metrics: AI-agent functionalities & capabilities, AI
      model types, #parameters of models, authentication/authorization
      policy, etc.

   We think the protocol stack of the AI-agent in an ACN should reside
   above the network & transport layers, which might be considered as in
   the application layer.  Correspondingly, the metrics specifically
   associated with AI-agents would be exchanged among AI-agents
   themselves, which makes the peering relationship for the AI-agent
   message exchanging different from that for the network and the
   compute metrics.





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   As shown in the Figure 1, the AI-agents reside on the App-layer,
   sitting above the network and compute entities.  The existing CATS
   metrics (network & compute) are targeted toward the traffic steering
   at the network layer, which might be achieved via the network
   protocol extension.  In comparison, the AI-agent metrics are
   generated for the overlay-exchange among App clients (those served as
   AI-agents), which are not generally subject to the signaling path
   over network protocols.


        ===> Extended CATS for ACN
           +-----------------+    AI-agent Metrics +-----------------+
           |     AI Agents   |<------------------->|     AI Agents   |
           +-----------------+                     +-----------------+
        ===> existing CATS here
           +-----------------+    Compute Metrics  +-----------------+
           | Compute-Entities|<------------------->| Compute-Entities|
           +-----------------+                     +-----------------+

           +-----------------+   Network Metrics   +-----------------+
           | Network Entities|<------------------->| Network Entities|
           +-----------------+                     +-----------------+


                     Figure 1: AI-agent Protocol Stack

   In the following section, we will define a CATS-based holistic model
   operating on general reference points that could be leveraged for the
   signaling exchanges of all the three types of metrics, i.e., network,
   compute and AI-agent metrics.

3.  CATS Reference Model for ACN

   The Section 2.1 provides use cases to explain why the CATS scheme may
   be applicable to optimize the AI-agent services in an ACN.  The
   Section 2.2 describes two existing CATS metrics, i.e., network and
   compute metrics, as well as defines a new metric type, i.e., the AI-
   agent metrics.  The same section also explains the protocol stack and
   the interactions among AI-agents themselves and between the CATS
   compute- & network- entities.  The uniqueness of AI-agents along with
   their instantiation states and the corresponding deployment models of
   ACNs make the associated metrics exchange model different from what
   is possibly adopted by the existing CATS metrics.  Thanks to the
   variations among the three metrics, we propose a holistic CATS
   reference model to accommodate the metrics extension of AI-agents in
   an ACN.





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   The Figure 2 demonstrates the integrated CATS reference architecture
   that accommodates the existing C-PS, C-NMA, C-SMA as well as the
   (new) AI-agents in ACN.  The AI-agent#0 is a physical-form AI-agent
   (e.g., embedded in a server) and the AI-agent#1 is a virtual instance
   deployed in the cloud service site#1.  The service instances #2, #3a
   and #3b are normal CATS instances deployed in the site#2 and site#3,
   respectively.  The CATS entity C-SMA-2 is in the service site#2 and
   the C-SMA-3 in the site#3, handling the capture, processing and
   distribution of compute metrics at service sites.  The provider
   network in the figure contains two CATS network metric agents, i.e.,
   C-NMA-2 and C-NMA-3, for the handling of network metrics.  Both
   C-SMAs and C-NMAs talk to the CATS entity C-PS for metrics
   distribution.  Here C-NMAs, C-SMAs and C-PS are defined in
   [CATS.Framework].

   When the CATS framework is extended to accommodate the AI-agent
   metrics in an ACN, there will be either a new type of CATS agent to
   be introduced, e.g., named as CATS AI-agent Metric Agent or C-AMA
   that can talk with C-PS, or the AI-agents directly engaging &
   communicating with C-PS.































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      C-NMA: CATS Network Metric Agent
      C-SMA: CATS Service Metric Agent
      C-AMA: CATS AI-agent Metric Agnet (new)

                                +------+ AI-agent
                                | AI-A | Inst.#1
                                +------+ (virtual)
                                   |
                            +------|--------+
                            |      o o o  (C-AMA-1)
                            |       \|/     |
                            |Service O Site1|
                            +--------|------+
                                     |                  +--------+
                                     |                  | Service|
                   Provider Network  |                  | Inst#2 |
                    +----------------|----------+       +--------+
                   /                 |           \         |
        (C-AMA-0) /          0.......O            \    +---|----------+
   +--------+    |           |                     |   | o o o    (C-SMA-2)
   |AI-agent|    |        (C-PS) ......(C-NMA-2)   |   |  \|/         |
   |  #0    |----|---O.......0            0--------|---|---O  Service |
   |(Physic.|    |           |                     |   |      Site 2  |
   +--------+    |           |                     |   +--------------+
                  \          0.....O(C-NMA-3)     /
                   \               |             /
                    +--------------|------------+
                                   |
                          +--------|------+
                  (C-SMA-3)Service O      |  +--------+
                          |Site 3 /|\     |  |Service |
                          |      o o o-------|Inst.#3a|
                          |      |        |  +--------+
                          +------|--------+
                                 |
                            +---------+
                            | Service |
                            | Inst.#3b|
                            +---------+


       Figure 2: CATS Reference Architecture for Integrated ACN









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3.1.  CATS Reference Model and Reference Points

   We propose to define a unified & generalized CATS reference model
   with reference points for the signaling process between CATS
   entities.  CATS entities consist of C-PS, C-NMA, C-SMA and, as
   introduced in the draft, C-AMA.  The reference points shall be
   standardised to support the functionalities and the interactions over
   the reference interfaces between CATS entities.

   The CATS reference model with reference points shall cover the
   following aspects:

   *  Service model: Proposed to be a producer-consumer model, with each
      CATS entity being either a producer or a consumer or both.

   *  Reference interfaces and points (between entities): definition,
      identity, name, parameters, etc.

   *  Singaling messages: definitions, parameters, types of exchanged
      messages (network-, compute-, and AI-agent metrics), etc.

   *  Singaling & management procedures: may include:

      -  CATS entity registration, authentication and authorization.

      -  Peer discovery and selection: the discovery scheme(s) can be
         from either the draft [IETF-Cisco-AIagent-draft] and the 3GPP
         NRF-like scheme [TS.23.501] that are applicable within the same
         domain, or the IETF MSDP-like scheme [RFC3618] applicable
         across domains.

      -  Peering session establishment, message-exchange, peering-state
         sync-up, peering-session update and modification, and peering
         release, etc.

   *  Overlay-based implementation and protocol stacks.  Please
      reference to the Section 2.2 for protocol stack discussion.

   *  Communication channels & implementation schemes, possibly being

      -  Authenticated APIs: For example: REST API methods (get, post,
         put, delete, etc.).

      -  Message brokers: a SW/HW intermediary, facilitating
         communication and data exchange between different CATS entities
         and AI-agents.





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3.2.  Examples of CATS Reference Points

   The Figure 3 applies the CATS reference model to exemplify the
   reference points and reference interfaces between different CATS
   entities and AI-agents.  For example, the reference point "RP_ps_nma"
   is between the C-PS and the C-NMA, and "RP_aia_ps" is between the AI-
   agent and the C-PS.  Note that the (c1) and (c2) are reference
   interfaces within the scope of their associated reference point.


          +---------------+                      +-------------+
          |   CATS C-PS   +(c1)------//------(c2)+ CATS C-NMA  |
          +---------------+      RP_ps_nma       +-------------+

          +---------------+                      +-------------+
          |  CATS C-NMA   +(c1)------//------(c2)+ CATS C-NMA  |
          +---------------+      RP_nma_nma      +-------------+

          +---------------+                      +-------------+
          |    AI-agent   +(c1)------//------(c2)+ CATS C-PS   |
          +---------------+      RP_aia_ps       +-------------+


        Figure 3: Reference points & interfaces btwn C-PS and C-NMA

4.  Security Considerations

   There is no security concern.

5.  IANA Considerations

   There is no IANA requirement.

6.  References

6.1.  Normative References

   [CATS-Metrics-Definition]
              Yao, K., et al., "CATS Metrics Definition",  draft-ietf-
              cats-metric-definition, March 2025.

   [CATS-PS-UseCase-Req]
              Yao, K., et al., "Computing-Aware Traffic Steering (CATS)
              Problem Statement, Use Cases, and Requirements",  draft-
              ietf-cats-usecases-requirements, June 2025.






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   [CATS.Framework]
              Li, C., et al., "A Framework for Computing-Aware Traffic
              Steering (CATS)",  draft-ietf-cats-framework, June 2025.

   [IETF-Cisco-AIagent-draft]
              Rosenberg, J., et al., "Framework, Use Cases and
              Requirements for AI Agent Protocols",  draft-rosenberg-ai-
              protocols, May 2025.

   [RFC3618]  Fenner, B., Ed. and D. Meyer, Ed., "Multicast Source
              Discovery Protocol (MSDP)", RFC 3618,
              DOI 10.17487/RFC3618, October 2003,
              <https://www.rfc-editor.org/info/rfc3618>.

   [TR.22.870]
              "3GPP TR 22.870 v0.3.0: Study on 6G Use Cases and Service
              Requirements; Stage 1, Rel-20",  3GPP TR 22.870, May 2025.

   [TR.23.700-14]
              "3GPP TR 23.700-14 v0.2.0: Study on Stage 2 for Integrated
              Sensing and Communication",  3GPP TR 23.700-14, June 2025.

   [TS.23.369]
              "3GPP TS 23.369: Architecture support for Ambient power-
              enabled Internet of Things; Stage 2",  3GPP TS 23.369,
              June 2025.

   [TS.23.501]
              "3GPP TS 23.501 (V19.0.0): System Architecture for 5G
              System; Stage 2",  3GPP TS 23.501, June 2024.

   [TS.23.502]
              "3GPP TS 23.502 (V19.0.0): Procedures for the 5G System;
              Stage 2",  3GPP TS 23.501, June 2024.

6.2.  Informative References

   [AI-Agent-6G-ARC]
              "Enabling Mobile AI Agent in 6G Era: Architecture and Key
              Technologies",  https://dl.acm.org/doi/abs/10.1109/
              MNET.2024.3422309, September 2024.

   [CMCC-ACN-WP]
              "AI-agent Communication Network White Paper",  CMCC ACN
              White Paper, July 2024.

Authors' Addresses




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   Tianji Jiang
   China Mobile
   Email: tianjijiang@yahoo.com


   Peng Liu
   China Mobile
   Email: liupengyjy@chinamobile.com











































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