DAWN M. A. Jadoon
Internet-Draft S. Robitzsch
Intended status: Informational InterDigital Europe Ltd
Expires: 6 May 2027 C. J. Bernardos
Universidad Carlos III de Madrid
2 November 2026
Discovery of AI Agents for Communication Services: Use Case and Problem
Statement
draft-jadoon-dawn-layered-agent-discovery-00
Abstract
This document presents a use case and problem statement for the
initial discovery of AI agents supporting end-to-end communication
services within an organization. The use case considers AI agents
associated with different functions of the Internet protocol suite,
including application, transport, Internet-layer, and related
network-operation functions. These agents may share general
capabilities but differ in their communication-related operational
scopes and responsibilities. The document examines the minimum
public discovery information needed to distinguish such agents within
a known organizational discovery scope.
Status of This Memo
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This Internet-Draft will expire on 6 May 2027.
Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 5
5. Security Considerations . . . . . . . . . . . . . . . . . . . 6
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
7. Informative References . . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
The Internet protocol suite separates application, transport,
Internet, and link-layer responsibilities [RFC1122] [RFC1123]. Each
layer performs specific functions and interacts with other layers
through established interfaces and protocols. This separation
supports interoperability and allows the implementation and evolution
of protocols independently of one another.
AI agents can augment these functions through adaptive decision-
making and automation within their respective operational boundaries.
For example, an application-associated agent may adapt media
generation according to application requirements, while a transport-
associated agent may assist with congestion control and transmission
behavior. Similarly, an Internet-layer-associated agent may assist
with routing and forwarding decisions, while agents associated with
network operations may support packet scheduling, prioritization, and
resource management.
In a layered agentic architecture, AI agents may operate
independently within their functional boundaries and may cooperate
with each other to support an end-to-end communication service. Each
agent may use observations and interfaces relevant to its associated
functions.
Research on cross-layer agentic networking illustrates cooperation
among specialized agents [SANET], while work on intent-aware
transport intelligence for immersive applications illustrates
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adaptation at a particular protocol layer [XR-TRANSPORT]. The
architectural motivation for agentic augmentation of the layered
protocol suite is further discussed in [LAYERED-AGENTS].
The ETSI Technology Radar identifies emerging ICT trends and assesses
their potential implications for future standardization activities
[ETSI-RADAR]. The deployment of specialized AI agents across
communication functions raises interoperability considerations when
agents need to cooperate to support shared service objectives. One
prerequisite for such cooperation is the ability to discover agents
exposing the required communication services and identify their
associated protocol-layer responsibilities.
Before such interactions can occur, a Discovering Entity may need to
obtain information about other AI agents that are discoverable within
a given organizational scope. Similar high-level descriptions of
communication-related services may refer to agents operating within
different functional and operational boundaries.
This use case considers initial discovery within an organization, one
of the deployment contexts identified by the DAWN Working Group
[DAWN-CHARTER]. Subsequent capability exchange, agent selection,
authorization, and operational coordination are outside the scope of
this document.
2. Terminology
This document uses the terminology defined in [DAWN-TERMS]. The
following additional terms are defined for the purposes of this
document.
Communication Function: In this document, a specialization of the
DAWN term _Function_, referring to processing that supports an
end-to-end communication service. Examples include application
adaptation, transport optimization, routing, forwarding, and
packet scheduling. A communication function describes the general
service provided by an agent, rather than its detailed
capabilities or internal implementation.
Layered Agentic Architecture: An architecture in which AI agents are
associated with different communication functions across the
Internet protocol suite and related network operations. These
agents may cooperate to support end-to-end communication service
objectives while operating within their respective functional
responsibilities.
Layer-Associated AI Agent: An AI agent whose advertised
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communication-related service is associated with one or more
protocol-layer functions or related network operations.
Operational Domain: An administrative or technological environment
within which an AI agent provides its advertised communication
function. An Operational Domain may be a subdivision of an
organization's Discovery Scope. It describes the context of the
agent's functional responsibility and does not, by itself,
establish authorization to interact with the agent.
In this document, the unqualified term _agent_ refers to an AI agent
unless otherwise stated.
3. Use Case
Consider an interactive augmented-reality (AR) application in an
organization supporting maintenance in an industrial facility. A
user device exchanges application traffic with a service through an
access network and one or more intermediate networks. The access may
be wired or wireless, and the service may be hosted locally or
remotely. Application traffic and network load can affect the
quality and continuity of the communication service.
The organization employs multiple agents to assist with different
aspects of the communication service. An application-associated
agent may adapt the resolution of AR content according to application
requirements. A transport-associated agent may monitor transport
performance and assist with transmission adaptation through
interfaces permitted by the relevant transport implementation.
Similarly, Internet-layer-associated agents may support routing and
forwarding operations, while agents associated with network
operations may assist with packet scheduling, prioritization, or
resource management.
These agents may use different observations, interfaces, and
operational controls. For example, an application-associated agent
may observe media quality and application performance, while a
transport-associated agent may observe round-trip time, loss, or
transmission behavior. A forwarding-associated agent may use routing
or topology information, whereas a scheduling-associated agent may
rely on queue occupancy, traffic priority, or link conditions.
Although these agents contribute to the same end-to-end communication
service, their operational responsibilities remain associated with
different communication functions.
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Within the organization, multiple AI agents may expose related high-
level service descriptions. For example, a transport-associated
agent and an Internet-layer-associated agent might both advertise
services concerned with traffic optimization. However, the former
may support transport adaptation, while the latter may support
routing or forwarding operations. The same high-level description
does not necessarily communicate these different protocol-layer
associations.
Suppose a transport-associated AI agent needs to discover other AI
agents exposing network-operation services within the organization's
infrastructure. Acting as the Discovering Entity, it initiates
discovery within the known organizational Discovery Scope. The
discovery mechanism may return information about multiple AI
resources that advertise related communication services.
For initial discovery, the relevant question is whether the returned
public information is sufficient to distinguish the advertised
communication functions and their protocol-layer associations, and to
provide the reachability information necessary for subsequent
interaction.
4. Problem Statement
The DAWN requirements draft identifies entity type, offered services,
reachability information, communication protocols, and relevant
security parameters as components of Minimum Discoverable Information
(MDI) [DAWN-REQ].
In the layered communication architecture described in Section 3, AI
agents may expose similar high-level service descriptions while
supporting different communication functions and protocol-layer
responsibilities. For example, a general description such as traffic
optimization may refer to an agent supporting transport adaptation,
routing, or packet scheduling. These agents may have different
operational scopes even though their advertised services are related.
The use case therefore raises the question of whether offered-service
information, together with the Discovery Scope and other available
discovery properties, is sufficient to distinguish the communication
functions and protocol-layer responsibilities associated with
discovered AI agents.
*Problem Statement:* What minimum public discovery information is
needed to distinguish and contact AI agents exposing similar
communication-related services but associated with different
protocol-layer or operational-domain responsibilities, within a known
organizational Discovery Scope?
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This problem concerns the information made available during initial
discovery rather than detailed capability assessment or agent
selection. Depending on the discovery mechanism, the necessary
distinctions might be expressed through offered-service descriptions,
existing discovery properties, or additional discovery context. This
document does not presume that new mandatory MDI fields or a
particular discovery architecture are required.
5. Security Considerations
This document describes an initial-discovery use case and does not
specify a discovery protocol or security mechanism.
6. IANA Considerations
This document makes no requests of IANA.
7. Informative References
[DAWN-CHARTER]
Internet Engineering Task Force, "Discovery of Agents With
Names (DAWN) Working Group Charter", 22 October 2026,
.
[DAWN-REQ] King, D. and A. Farrel, "Requirements for the Discovery of
Agents With Names (DAWN)", Work in Progress, Internet-
Draft, draft-king-dawn-requirements-02, 19 October 2026,
.
[DAWN-TERMS]
Farrel, A., Yao, K., Schott, R., and N. Williams,
"Terminology for the Discovery of Agents With Names
(DAWN)", Work in Progress, Internet-Draft, draft-farrel-
dawn-terminology-06, 28 October 2026,
.
[ETSI-RADAR]
European Telecommunications Standards Institute (ETSI),
"ETSI Technology Radar", n.d.,
.
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[LAYERED-AGENTS]
Jadoon, M. A., Robitzsch, S., and C. J. Bernardos,
"Agentic AI Architectural Principles for Autonomous
Computer Networks", Work in Progress, Internet-Draft,
draft-jadoon-nmrg-agentic-ai-autonomous-networks-00, 2
March 2026, .
[RFC1122] Braden, R., Ed., "Requirements for Internet Hosts -
Communication Layers", STD 3, RFC 1122,
DOI 10.17487/RFC1122, October 1989,
.
[RFC1123] Braden, R., Ed., "Requirements for Internet Hosts -
Application and Support", STD 3, RFC 1123,
DOI 10.17487/RFC1123, October 1989,
.
[SANET] Xiao, Y., Li, X., Zhou, H., Li, Y., Gao, Y., Shi, G.,
Zhang, P., and M. Krunz, "SANet: A Semantic-Aware Agentic
AI Networking Framework for Cross-Layer Optimization in
6G", IEEE Transactions on Mobile Computing Vol. 25, 2026,
DOI 10.1109/TMC.2026.3691804, 2026,
.
[XR-TRANSPORT]
Huynh, V. S. H., Qian, P., Wang, N., Udora, C., and R.
Tafazolli, "User-Intent Aware Transport-Layer Intelligence
for Frame Synchronisation in Multi-Party XR Application",
IEEE Gaming, Entertainment, and Media Conference 2024,
DOI 10.1109/GEM61861.2024.10585795, 2024,
.
Authors' Addresses
Muhammad Awais Jadoon
InterDigital Europe Ltd
London
United Kingdom
Email: muhammad.awaisjadoon@interdigital.com
Sebastian Robitzsch
InterDigital Europe Ltd
London
United Kingdom
Email: sebastian.robitzsch@interdigital.com
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Carlos J. Bernardos
Universidad Carlos III de Madrid
Madrid
Spain
Email: cjbc@it.uc3m.es
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