Dynamic Multi-agents Secured Collaboration Infrastructure architecture
draft-li-dmsc-inf-architecture-02
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| Authors | Xueting Li , Aijun Wang | ||
| Last updated | 2026-01-14 | ||
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draft-li-dmsc-inf-architecture-02
DMSC Working Group X. Li
Internet-Draft A. Wang
Intended status: Standards Track China Telecom
Expires: 19 July 2026 15 January 2026
Dynamic Multi-agents Secured Collaboration Infrastructure architecture
draft-li-dmsc-inf-architecture-02
Abstract
This document presents an architectural framework for dynamic multi-
agent collaboration from an infrastructure perspective. It outlines
the network requirements introduced by large-scale agent
collaboration, and proposes a systematic approach to enabling Dynamic
Multi-agent Secured Collaboration (DMSC) through infrastructure
capabilities. The architecture focuses on how network control and
forwarding functions can actively participate in agent collaboration.
Status of This Memo
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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. Conventions used in this document . . . . . . . . . . . . . . 3
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. Network Requirements . . . . . . . . . . . . . . . . . . . . 4
5. DMSC Infrastructure Architecture . . . . . . . . . . . . . . 4
5.1. DMSC Infrastructure Architecture . . . . . . . . . . . . 5
6. Infrastructure Functions Enabling Active Network
Participation . . . . . . . . . . . . . . . . . . . . . . 8
6.1. Agent Identification and Classification . . . . . . . . . 8
6.2. Infrastructure-Level Agent Discovery . . . . . . . . . . 9
6.3. Semantic Request Routing . . . . . . . . . . . . . . . . 9
6.4. Secure Collaboration Context Propagation . . . . . . . . 10
6.5. Operational Visibility . . . . . . . . . . . . . . . . . 10
7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 11
8. Security Considerations . . . . . . . . . . . . . . . . . . . 11
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
10. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 11
11. Normative References . . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
Intelligent agents have evolved rapidly in recent years, driven by
advances in artificial intelligence models, computing platforms, and
network connectivity. Early forms of agents were typically embedded
within isolated systems and designed to perform narrowly defined
tasks under predefined conditions. Their interactions with external
entities were limited and often mediated by tightly coupled
application logic [IoA].
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With the increasing availability of large-scale AI models, edge
computing resources, and programmable network infrastructures, agents
are becoming more autonomous, adaptive, and capable of operating
across distributed environments. Modern agents can perceive changes
in their environment, make decisions based on local or shared
information, and interact with other agents and tools in order to
achieve complex objectives. These interactions are no longer
confined to static configurations or single administrative domains,
but increasingly span devices, networks, and application platforms.
As agents continue to proliferate, they are forming large-scale
collaborative systems in which multiple agents dynamically discover
each other, exchange information, and coordinate actions. Such
systems exhibit highly dynamic behavior, including frequent changes
in agent population, roles, and interaction patterns. The resulting
agent ecosystems resemble an open, interconnected environment rather
than a collection of isolated applications.
The evolution toward large-scale, dynamic agent ecosystems introduces
new challenges for the underlying network infrastructure. While
agents are capable of sophisticated reasoning and decision-making,
their ability to collaborate effectively depends on the availability
of common, scalable, and interoperable networking support.
This document focuses on the architectural aspects of enabling
dynamic multi-agent collaboration from a network and infrastructure
perspective. It examines how network control and forwarding
functions can be extended to recognize agents as first-class entities
and provide generic support for agent identification, discovery,
semantic-aware communication, and coordination. The architecture is
intended to support a wide range of agent types, including on-device
agents, network-resident agents, and third-party agents, without
imposing assumptions about their internal implementation.
The scope of this document is limited to architectural concepts and
functional building blocks. It does not define specific protocols,
data models, or security mechanisms, nor does it prescribe particular
deployment scenarios or application workflows. Instead, it provides
a foundational framework upon which more detailed specifications,
including protocol designs and security architectures, can be
developed in subsequent documents.
2. Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119] .
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3. Terminology
The following terms are defined in this draft:
* DMSC: Dynamic Multi-agent Secured Collaboration. The framework
and infrastructure enabling secure and efficient collaboration
among dynamic agents.
* Agent: An autonomous software entity capable of perception,
planning, decision-making, and execution.
* SemR: Semantic Routing. The process of routing an Agent request
based on the meaning or intent of the request, rather than solely
on a pre-defined address or identifier.
4. Network Requirements
The proliferation of intelligent agents fundamentally reshapes
interaction patterns and control dynamics in future networks. Agent
interactions are typically short-lived, context-dependent, and driven
by task semantics rather than static endpoints. Moreover, agents may
dynamically join or leave collaborative groups, migrate across
administrative domains, or change roles over time. These
characteristics introduce new requirements for network
infrastructures, including agent-level identity management,
capability-aware communication, scalable registration and discovery,
cross-domain collaboration support, and adaptive routing, as also
reflected in [draft-yu-ai-agent-use-cases-in-6g].[usecase]
Collectively, these requirements indicate that future networks must
go beyond passive connectivity and actively support dynamic multi-
agent collaboration. The core idea of Dynamic Multi-agent Secured
Collaboration (DMSC) is to elevate key collaboration-related
functions into the network infrastructure. Instead of embedding all
coordination logic within applications or agent frameworks, DMSC
leverages infrastructure-level capabilities exposed through control-
plane and forwarding-plane functions. This approach enables the
network to recognize agents as first-class entities, maintain high-
level collaboration context, and make informed decisions on
discovery, routing, and coordination support in a scalable and
interoperable manner.
5. DMSC Infrastructure Architecture
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5.1. DMSC Infrastructure Architecture
Figure 1 illustrates the overall architecture for dynamic multi-agent
collaboration from an infrastructure-centric perspective. The
architecture positions the network infrastructure as an active
participant in agent collaboration, while preserving the autonomy and
task-level reasoning of individual agents. In this architecture, the
network does not execute agent logic or interpret task semantics.
Instead, it provides generic support functions that enable agents to
collaborate more efficiently and reliably. Agents remain autonomous,
while the network supplies shared infrastructure capabilities.
From an infrastructure perspective, the architecture is organized
into three logical layers:
* Management Plane: governs policies, trust, lifecycle and
authentication aspects.
* Control Plane: Manages agent identity, discovery, policies, and
collaboration context.
* Forwarding Plane: Supports semantic-aware routing and data
forwarding for agent interactions.
* Coordination Support Functions: Provide higher-level abstractions
that bridge agent collaboration and network operation.
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+-------------------------------------------------------------------------------------+
| Management & Orchestration Plane |
| +----------------+ +------------------+ +-----------------+ +--------------+ |
| | Policy Manager | | Lifecycle Mgmt | | Observability & | | Agent | |
| | (Rules, Trust) | | (Agent, Context) | | Analytics | |Authentication| |
| +----------------+ +------------------+ +-----------------+ +--------------+ |
+-------------------------------------------------------------------------------------+
| ^
Collaboration Context | |
v |
+----------------------------------------------------------------------------------------------------------------------------------------------+
| Network Infrastructure |
| +-----------------------------------------------------------------+ +------------------------------------------+ +-----------------------+ |
| | Node1 | | Node 2 | | Node 3 | |
| | +------------------------+ +-------------------------------+ | | +--------------+ +--------------------+ | |+-------------+ +-----+| |
| | | Control Plane | | Coordination Support | | | |Control Plane | |Coordination Support| | ||Control Plane| |... || |
| | |------------------------| |-------------------------------| | | |--------------| |--------------------| | |+-------------+ +-----+| |
| | | - Agent Identity |<-->| - Collaboration Context | | | | ... |-| ... | | ||... | |... || |
| | | - Agent Classification | | - Policy & Consistency | | | | | | | | || | | || |
| | | - Registration | | - Cross-domain Coordination | | | | | | | | || | | || |
| | | - Discovery Control | +-------------------------------+ | | +--------------+ +--------------------+ | |+-------------+ +-----+| |
| | +------------------------+ ^ | | | ^ | | | ^ | |
| | | | | | | | | | | | | |
| | | Control & Policy | Context Propagation | | | Control | Context | | ... | ... | | |
| | v | | | v & Policy | Propagation | | v | | |
| | +-------------------------------------------------------------+ | | +---------------------------+ | | +-----------------+ | |
| | | Forwarding Plane | | | | Forwarding Plane | | | |Forwarding Plane | | |
| | |-------------------------------------------------------------| | | |---------------------------+ | | |-----------------| | |
| | | - Semantic Request Routing | | | | ... | | | |... | | |
| | | - Capability-aware Forwarding | | | | | | | | | | |
| | | - Multi-hop Collaboration Paths | | | | | | | | | | |
| | | - Dynamic Redirection & Adaptation | | | | | | | | | | |
| | +-------------------------------------------------------------+ | | +---------------------------+ | | +-----------------+ | |
| +-----------------------------------------------------------------+ +------------------------------------------+ +-----------------------+ |
+----------------------------------------------------------------------------------------------------------------------------------------------+
| |
| Agent-to-Agent Communication | Agent-to-Agent Communication
v v
+--------------------+ +--------------------+ +--------------------+ +------------------+ +-----------------+
| Agent A |<->| Agent B | | Agent C |<->| Agent B |<->| Agent C |
|--------------------| |--------------------| |--------------------| |------------------| |-----------------|
| - Identity | | - Identity | | ... | | ... | | ... |
| - Capabilities | | - Capabilities | +--------------------+ +------------------+ +-----------------+
| - Local Reasoning | | - Local Reasoning |
+--------------------+ +--------------------+
Figure 1 The infrastructure architecture of dynamic multi-agent collaboration
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At the top of the architecture, agents engage in collaborative
activities driven by task intents, shared goals, and contextual
information. Agents are responsible for local reasoning, decision-
making, and execution of task-specific logic. The network does not
interpret agent semantics or execute agent logic; instead, it
provides common infrastructure capabilities that support efficient
and scalable collaboration among agents. Above the network
infrastructure, a Management and Orchestration Plane provides non-
real-time management functions, including policy management, agent
and context lifecycle management, observability and analytics, and
agent authentication support. This plane supplies policy, trust, and
state-related inputs to the network infrastructure.
The network infrastructure itself is composed of multiple network
nodes, each implementing a common set of logical functions. Within
each node, the Control Plane provides agent-aware control functions,
including agent identity management, classification, registration,
and discovery control. These functions enable the network to
recognize agents as first-class entities and maintain a consistent
view of agent-related information across the infrastructure. By
decoupling agent identity from physical location, the control plane
supports dynamic agent lifecycle events such as mobility,
instantiation, and termination.
Complementing the control plane, Coordination Support Functions
maintain and propagate collaboration context at an abstract level.
This includes information related to collaboration state, policy
constraints, and cross-domain consistency. Coordination support
functions do not encode task semantics but provide a common substrate
for maintaining coherence among dynamic collaboration activities,
particularly when agents operate across administrative or network
domains.
The Forwarding Plane extends traditional packet forwarding by
incorporating semantic-aware decision-making. Instead of relying
solely on static addresses, forwarding decisions may consider agent
capabilities, collaboration context, and network conditions. This
enables semantic request routing, multi-hop collaboration paths, and
dynamic redirection when agent availability or network conditions
change. Such capabilities are essential for supporting adaptive and
resilient agent collaboration at scale.
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Agent-to-Agent communication flows traverse the forwarding plane,
while control and context information is exchanged through
interactions with control-plane and coordination functions. The
separation of concerns among agents, control functions, and
forwarding functions ensures that agent autonomy is preserved, while
the network provides reusable and interoperable support for
collaboration.
Overall, this architecture establishes a clear division of
responsibilities: agents focus on intelligent behavior and task
execution, while the network infrastructure supplies agent-aware
control, semantic-aware forwarding, and coordination support. This
division enables dynamic multi-agent collaboration to scale across
heterogeneous environments and evolve independently of specific agent
implementations.
6. Infrastructure Functions Enabling Active Network Participation
6.1. Agent Identification and Classification
In large-scale dynamic multi-agent environments, agents cannot be
effectively supported using traditional host- or service-based
identifiers alone. Agents may be instantiated dynamically, migrate
across network locations, or operate concurrently on the same
physical node. As a result, the network requires a mechanism to
identify agents as logical entities that are decoupled from network
topology.
The proposed architecture introduces network-visible agent
identifiers that represent agents independently of their physical
location or hosting environment. These identifiers enable the
network to consistently recognize agents across control and
forwarding functions, even as underlying network bindings change.
Beyond basic identification, the architecture supports agent
classification based on capabilities, roles, and contextual
attributes. Classification information may describe, for example,
whether an agent operates on a device, within the network, or as a
third-party service, as well as the functional roles it can assume in
collaborative processes. Such information is not intended to expose
internal agent logic, but to provide sufficient abstraction for
network-level decision-making.
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6.2. Infrastructure-Level Agent Discovery
Agent discovery is a fundamental prerequisite for collaboration, yet
traditional discovery mechanisms are typically designed for
relatively static services or tightly scoped environments. In
contrast, multi-agent collaboration requires discovery mechanisms
that can operate across heterogeneous platforms, adapt to dynamic
agent populations, and respect administrative boundaries.
In DMSC architecture, agent discovery is provided as an
infrastructure-level function, rather than being entirely implemented
within agent frameworks. The network supports discovery queries
based on agent identifiers, advertised capabilities, policy
constraints, and dynamic state information. This allows agents to
locate suitable collaborators without requiring global knowledge or
centralized coordination. Discovery mechanisms may differ between
intra-domain and inter-domain contexts. Within a domain, discovery
may leverage localized registries or control-plane functions for
efficiency. Across domains, discovery must account for policy,
trust, and information exposure constraints, potentially relying on
aggregated or abstracted representations of agent capabilities.
6.3. Semantic Request Routing
Traditional routing mechanisms forward packets based on destination
addresses without awareness of application intent or collaboration
context. However, in dynamic multi-agent collaboration, interactions
are often driven by what is requested rather than where a specific
endpoint is located. The DMSC architecture introduces semantic
request routing, where requests can be expressed in terms of agent
capabilities, roles, or collaboration context. The network
forwarding plane may use such semantic information, together with
network conditions and policy constraints, as input to routing and
forwarding decisions.
Semantic routing enables several advanced behaviors. Requests may be
dynamically directed to different agents capable of fulfilling a
given role, rather than a fixed endpoint. Multi-hop collaboration
paths can be constructed, where intermediate agents contribute
partial results. When agent availability or network conditions
change, requests can be redirected without requiring agents to
reinitiate discovery. Importantly, semantic routing does not require
the network to interpret task semantics or agent logic. The network
operates on abstracted descriptors and policies, enabling adaptive
and resilient collaboration while preserving agent autonomy.
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6.4. Secure Collaboration Context Propagation
Effective collaboration among dynamic agents requires shared context,
such as session state, coordination constraints, and policy
information. When collaboration spans multiple domains or network
segments, maintaining consistent context becomes increasingly
challenging. The DMSC architecture supports collaboration context
propagation at the infrastructure level. Context information
associated with a collaboration can be attached to control-plane
interactions and, where appropriate, influence forwarding-plane
behavior.
This enables the network to maintain coherence across dynamic
collaboration activities without requiring agents to explicitly
manage all contextual information. Security-related attributes, such
as authorization scope or policy constraints, may be bound to
collaboration context to ensure that interactions remain consistent
with domain-specific requirements. In cross-domain scenarios,
context propagation mechanisms support controlled translation or
abstraction to maintain interoperability while respecting local
policies.
6.5. Operational Visibility
As multi-agent systems scale, the lack of visibility into
collaboration-level behavior becomes a significant operational
challenge. Traditional network observability focuses on flows or
endpoints, offering limited insight into agent interactions and
coordination dynamics. The DMSC architecture introduces operational
visibility at the collaboration level. Observable entities include
agent interactions, coordination relationships, and their association
with network resources and conditions. This visibility is not
intended to expose agent internals, but to provide sufficient
information for monitoring, troubleshooting, and optimization.
Operational visibility enables feedback-driven adaptation.
Information collected by the infrastructure can inform control-plane
decisions, such as adjusting discovery policies or routing
preferences, and forwarding-plane behavior, such as load-aware
redirection. Over time, this feedback loop supports continuous
optimization of collaboration efficiency and network resource
utilization. At the same time, the architecture recognizes that
increased visibility introduces potential risks, which are addressed
at the architectural level through controlled exposure and policy
mechanisms.
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7. Conclusion
This document presents an infrastructure-centric architecture for
dynamic multi-agent collaboration. By introducing agent-aware
abstractions into network control and forwarding functions, the
architecture enables scalable discovery, semantic-aware
communication, and coordination support without constraining agent
autonomy or interpreting agent semantics. The proposed framework
defines clear architectural boundaries between agent intelligence and
network responsibility, and provides a common foundation for
subsequent protocol, security, and deployment-specific specifications
that support the evolution of the Internet of Agents.
8. Security Considerations
This architecture introduces several security considerations,
including risks related to agent identity spoofing, capability
misrepresentation, semantic routing manipulation, cross-domain trust
inconsistencies, and information leakage through enhanced
observability. Detailed security mechanisms are outside the scope of
this document.
9. IANA Considerations
TBD
10. Acknowledgement
TBD
11. Normative References
[IoA] L, J., "Internet of Agents – Definition, Architecture and
Applications.
https://aip.openatom.tech/explore/journalism/
detail/501037383572131840", October 2025.
[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>.
[usecase] Y, M., "draft-yu-ai-agent-use-cases-in-6g.
https://datatracker.ietf.org/doc/html/draft-yu-dmsc-ai-
agent-use-cases-in-6g", July 2025.
Authors' Addresses
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Xueting Li
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: lixt2@foxmail.com
Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: wangaj3@chinatelecom.cn
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