Problem Statement and Gap Analysis for Autonomic Networking with AI- powered Autonomic Service Agent
draft-han-anima-gap-analysis-ai-asa-00

anima                                                        M. Han, Ed.
Internet-Draft                                              China Unicom
Intended status: Standards Track                           T. Huang, Ed.
Expires: 4 January 2027                                        CNIC, CAS
                                                            J. Zhao, Ed.
                                                            China Unicom
                                                             3 July 2026

  Problem Statement and Gap Analysis for Autonomic Networking with AI-
                    powered Autonomic Service Agent
                 draft-han-anima-gap-analysis-ai-asa-00

Abstract

   This document presents a problem statement and gap analysis of the
   technical challenges faced by the GeneRic Autonomic Signaling
   Protocol (GRASP) [RFC8990] when it is used, within the Autonomic
   Networking Infrastructure (ANI) defined by the ANIMA Working Group
   [RFC8993], to support enhanced Autonomic Service Agents that
   incorporate Large Language Model (LLM) capabilities, referred to in
   this document as AI-ASAs.

Status of This Memo

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   This Internet-Draft will expire on 4 January 2027.

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Table of Contents

   1.  Introduction and Characteristics  . . . . . . . . . . . . . .   2
   2.  Problem Statement and Gap Analysis  . . . . . . . . . . . . .   3
     2.1.  Insufficient Payload Capacity for Large-Scale Semantic Data
           Transfer  . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.2.  Objective Semantics Are Not Flexible  . . . . . . . . . .   3
   3.  Potential Areas for Future Work . . . . . . . . . . . . . . .   3
     3.1.  Large-Scale Information Transfer  . . . . . . . . . . . .   4
     3.2.  Self-Describing Metadata for Objectives . . . . . . . . .   4
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .   4
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   4
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   4
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   4
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   5
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   5

1.  Introduction and Characteristics

   Within the Autonomic Networking Infrastructure (ANI) framework
   defined by [RFC8993], the Autonomic Control Plane (ACP) [RFC8994],
   and the GeneRic Autonomic Signaling Protocol (GRASP) [RFC8990], the
   Autonomic Service Agent (ASA) is a core logical component running on
   an autonomic node.

   As embedded AI and edge inference capabilities are introduced into
   the network control plane, enhanced ASAs with LLM-based reasoning
   capabilities, referred to as AI-powered Autonomic Service Agent (AI-
   ASAs) [I-D.han-anima-ai-asa], are beginning to emerge.  Communication
   between these agents has peer-to-peer characteristics that differ
   from those of traditional hard-coded network components, and this
   creates new workload requirements for the existing signaling plane:

   *  Large information transfers: Signaling exchanges may need to carry
      variable-length context sequences, such as reasoning traces,
      prompt variables, or abstract intent descriptions.  A single
      Objective payload may reach several megabytes.

   *  Objective-oriented interaction: The information exchanged and
      negotiated between nodes is no longer limited to traditional
      structured resource parameters, such as the IPv6 prefix length

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      defined in [RFC8992].  It may instead consist of abstract semantic
      intents describing high-level network Objectives, or natural-
      language interactions.

2.  Problem Statement and Gap Analysis

2.1.  Insufficient Payload Capacity for Large-Scale Semantic Data
      Transfer

   Problem: In current GRASP implementations and deployments, Objective
   payloads are usually tens or hundreds of bytes in size.  The protocol
   was not considered for contexts in which payloads may reach the
   megabyte level.  In practical AI-ASA collaboration, the amount of
   data exchanged may increase sharply, while the current mechanism does
   not provide application-layer support for advertising data size,
   negotiating a receive window, or transferring data in chunks.  This
   may cause memory overflow at the receiver and may also lead to head-
   of-line blocking within the ACP.

   Gap:The data transfer pattern also needs to be distinguished.  AI-
   ASAs may use short request-response exchanges, but they may also
   share information continuously, for example by streaming real-time
   inference-state updates.  The existing bulk-transfer draft
   [I-D.carpenter-anima-grasp-bulk] focuses mainly on one-time transfers
   of large data blocks.  Continuous, low-latency delivery in streaming
   scenarios therefore requires further discussion.

2.2.  Objective Semantics Are Not Flexible

   Problem: Each Objective must also be registered in advance.  This
   static model cannot meet the need for AI-ASAs to generate new
   Objectives dynamically or to describe the meaning of an Objective by
   themselves.  It therefore limits flexible and open semantic
   interaction between LLM-based agents.  Gap: The current GRASP
   specification relies on strictly predefined CBOR encodings and fixed
   semantic mappings for Objectives, as illustrated by [RFC8992].

3.  Potential Areas for Future Work

   To minimize the development and deployment cost for the working group
   and existing networks, while maintaining the stability of the current
   ANI control plane, future work could examine backward-compatible
   extensions at the application layer.  Such work should not change the
   state-transition logic of the GRASP core state machine, modify the
   underlying ACP transport protocol, or define how specific intent
   semantics are interpreted.

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3.1.  Large-Scale Information Transfer

   This issue is related to the ANIMA Working Group draft on bulk data
   transfer [I-D.carpenter-anima-grasp-bulk].  That draft proposes a
   general method for transferring bulk data by using GRASP itself and
   is intended for cases in which the data is too large to fit in a
   normal GRASP message.  It is also related to the large-scale
   information delivery requirements discussed in
   [I-D.ietf-anima-grasp-distribution].

3.2.  Self-Describing Metadata for Objectives

   Option 1: Add optional self-describing metadata to an Objective.  The
   sender could include machine-readable type information, such as
   objective-type and semantic-version, together with the Objective,
   allowing the receiving AI-ASA to identify and parse a new Objective
   that has not been predefined.

   Option 2: One approach is to define a generic Objective and allow its
   content to be generated by AI based on the existing Objective format.
   Another approach is to allow AI agents to communicate with each other
   in natural language.  At the gap-analysis stage, both approaches, as
   well as other possible approaches, can be discussed.

4.  Security Considerations

   TBD.

5.  IANA Considerations

   TBD.

6.  Acknowledgements

   TBD

7.  References

7.1.  Normative References

   [RFC8990]  Bormann, C., Carpenter, B., Ed., and B. Liu, Ed., "GeneRic
              Autonomic Signaling Protocol (GRASP)", RFC 8990,
              DOI 10.17487/RFC8990, May 2021,
              <https://www.rfc-editor.org/info/rfc8990>.

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   [RFC8993]  Behringer, M., Ed., Carpenter, B., Eckert, T., Ciavaglia,
              L., and J. Nobre, "A Reference Model for Autonomic
              Networking", RFC 8993, DOI 10.17487/RFC8993, May 2021,
              <https://www.rfc-editor.org/info/rfc8993>.

   [RFC8494]  Wilson, D. and A. Melnikov, Ed., "Multicast Email (MULE)
              over Allied Communications Publication (ACP) 142",
              RFC 8494, DOI 10.17487/RFC8494, November 2018,
              <https://www.rfc-editor.org/info/rfc8494>.

7.2.  Informative References

   [RFC8992]  Jiang, S., Ed., Du, Z., Carpenter, B., and Q. Sun,
              "Autonomic IPv6 Edge Prefix Management in Large-Scale
              Networks", RFC 8992, DOI 10.17487/RFC8992, May 2021,
              <https://www.rfc-editor.org/info/rfc8992>.

   [I-D.carpenter-anima-grasp-bulk]
              Carpenter, B. E., Jiang, S., and B. Liu, "Transferring
              Bulk Data over the GeneRic Autonomic Signaling Protocol
              (GRASP)", Work in Progress, Internet-Draft, draft-
              carpenter-anima-grasp-bulk-05, 9 January 2020,
              <https://datatracker.ietf.org/doc/html/draft-carpenter-
              anima-grasp-bulk-05>.

   [I-D.ietf-anima-grasp-distribution]
              Jiang, S., Liu, B., Xiao, X., Hecker, A., and X. Zheng,
              "Information Distribution over GRASP", Work in Progress,
              Internet-Draft, draft-ietf-anima-grasp-distribution-12, 11
              December 2024, <https://datatracker.ietf.org/doc/html/
              draft-ietf-anima-grasp-distribution-12>.

   [I-D.han-anima-ai-asa]
              Han, M., Zhang, N., and J. Zhao, "Considerations of AI-
              powered Autonomic Service Agent Communication", Work in
              Progress, Internet-Draft, draft-han-anima-ai-asa-01, 15
              January 2026, <https://datatracker.ietf.org/doc/html/
              draft-han-anima-ai-asa-01>.

Authors' Addresses

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

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   Tianyi Huang (editor)
   CNIC, CAS
   Beijing
   China
   Email: tyhuang@cnic.cn

   Jing Zhao (editor)
   China Unicom
   Beijing
   China
   Email: zhaoj501@chinaunicom.cn

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