Semantic Routing Architecture for AI Agents Communication
draft-li-semantic-routing-architecture-00
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| Authors | Xueting Li , Aijun Wang | ||
| Last updated | 2025-11-03 | ||
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draft-li-semantic-routing-architecture-00
Working Group X. Li
Internet-Draft A. Wang
Intended status: Standards Track China Telecom
Expires: 8 May 2026 4 November 2025
Semantic Routing Architecture for AI Agents Communication
draft-li-semantic-routing-architecture-00
Abstract
This document introduces an Semantic Routing (SR) Architecture for
enabling intelligent, semantic-driven communication among AI Agents.
Unlike traditional IP-based routing or service mesh approaches, SRA
leverages application-layer semantics — including service identity,
intent vectors, and trust scores — to guide routing decisions
dynamically. The architecture supports intent-driven task
collaboration, trust-aware policy enforcement, and adaptive routing
for multi-agent environments. SRA enables the network to evolve from
a passive transport layer to an intelligent collaboration substrate
supporting multi-agent coordination and cognitive networking.
Status of This Memo
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Please review these documents carefully, as they describe your rights
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and restrictions with respect to this document. Code Components
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 4
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. Architecture Overview . . . . . . . . . . . . . . . . . . . . 4
5. Functional Layers and Design Principles . . . . . . . . . . . 6
6. Control and Forwarding Procedures . . . . . . . . . . . . . . 7
7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 9
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 10
10. Normative References . . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
The emergence of AI-driven ecosystems has transformed communication
paradigms across computing and networking infrastructures.
Traditional routing systems, designed for host-to-host communication,
focus on connectivity, reachability, and link-state optimization.
However, in environments where AI agents [AIAgent]—autonomous
entities with reasoning and goal-oriented behavior—interact
dynamically, such topological routing no longer meets the operational
needs. Each agent represents not only a computational endpoint but
also a semantic actor that generates intents, expresses capabilities,
and negotiates tasks. The network must therefore evolve from a
static forwarding fabric into a semantic coordination plane capable
of interpreting meaning, context, and trust.
Existing frameworks such as Service Mesh [ServiceMesh] (e.g., Istio
[Istio], Linkerd) and Software-Defined Networking (SDN) have improved
visibility and control but remain largely syntactic. They route
requests based on service names, APIs, or labels, not on why the
communication occurs or what semantic goal it represents. For
example, in an AI multi-agent system performing distributed
reasoning, the decision of which node to contact depends on task
semantics—such as “model adaptation,” “policy refinement,” or “data
summarization”—and on dynamic factors like capability, latency, and
trustworthiness. None of these can be expressed using IP addresses
or conventional service identifiers.
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The Semantic Routing (SR) architecture introduced in this draft aims
to bridge this semantic gap. It extends routing intelligence from
the network layer to the application layer, enabling communication
decisions based on intent vectors, policy interpretation, and trust
evaluation. Through a semantic control plane, SRA aligns network
behavior with business and computational objectives, providing
adaptive, secure, and efficient routing among AI agents. This
enables networks to support intent-aware task collaboration and to
act as intelligent participants in distributed cognition processes.
SRA also addresses emerging challenges of large-scale agent
communication, including semantic interoperability, cross-domain
trust, and self-optimization. Modern AI ecosystems consist of
heterogeneous nodes—cloud agents, edge assistants, embedded inference
units—that collaborate under uncertain conditions. Routing must thus
adapt to fluctuating workloads, mobility, and trust contexts. Static
or location-based approaches cannot efficiently manage such dynamism.
By integrating semantic interpretation with continuous telemetry
feedback, SRA allows networks to self-optimize: routes are
recalculated not only based on network states (e.g., congestion or
delay) but also on semantic relevance and agent reliability.
The design of SRA is guided by several fundamental objectives:
* Semantic Awareness – Networks should understand and act upon high-
level intents derived from AI tasks.
* Trust Integration – Routing should consider the reliability and
historical behavior of agents.
* Dynamic Adaptation – Telemetry-driven feedback loops must
continuously refine routing decisions.
* Backward Compatibility – SAR should coexist with IP, BGP, and
service-mesh infrastructures.
* Distributed Autonomy – Each semantic router should make local
decisions while aligning with global intent policies.
By embedding intelligence into the control and forwarding planes, SRA
transforms the Internet from a data transport medium into a
collaborative semantic ecosystem that supports intelligent
communication for the next generation of distributed AI systems.
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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] .
3. Terminology
The following terms are defined in this draft:
* SRA (Semantic Routing Architecture): The routing framework defined
in this document integrating semantic awareness, trust, and policy
control.
* AI Agent: An autonomous software entity capable of making context-
based decisions, performing actions, and communicating with other
agents.
* Intent Vector: A structured representation of the communication
goal, expressed semantically (e.g., task type, priority, resource
needs).
* Semantic Router (SR): Entity interpreting intent metadata and
enforcing semantic forwarding policies.
* Semantic Forwarding Table (SFT): Forwarding table mapping intent
categories to next hops and constraints.
4. Architecture Overview
The Semantic Router (SR) architecture introduces a layered design
that bridges application semantics and network operation. It defines
a semantic control framework capable of understanding agent-generated
intents, evaluating contextual trust, and translating these into
actionable routing policies. At its core, SRA consists of four
interacting planes—Application, Control, Data, and Feedback—each
responsible for distinct yet interdependent functions.
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The Application Plane hosts AI agents that issue Intent Vectors (IVs)
representing goals such as “request model inference” or “synchronize
state.” The Semantic Control Plane collects these intents,
authenticates identities, and maps them to routing policies via the
Policy Engine (PE). These policies are then propagated to Semantic
Routers (SRs) in the Data Plane, which execute the forwarding logic
using Semantic Forwarding Tables (SFTs) that link intent types to
paths and constraints. Finally, the Feedback Plane, driven by
Telemetry Agents (TAs), monitors latency, trust, and service quality,
feeding the results back into the control plane for continuous
optimization.
This closed-loop system ensures that SAR continuously aligns network
operation with evolving task goals. The architecture is designed to
integrate seamlessly with existing IP and SDN environments, relying
on overlays or extended routing attributes (e.g., BGP communities or
SRv6 tags) to express semantic metadata.
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+-------------------------------------------+
| Application Plane |
|-------------------------------------------|
| +-----------+ +-----------+ |
| | Agent A |<--->| Agent B | |
| +-----------+ +-----------+ |
| ^ ^ |
| | Intent Vector |Intent Vector|
+---------|-------------------|-------------+
| |
v v
+-------------------------------------------+
| Control Plane |
|-------------------------------------------|
| +--------------+ +------------------+ |
| | Intent Ctrl |<->| Policy Engine | |
| +--------------+ +------------------+ |
| ^ ^ |
| | | |
| v v |
| +-----------+ +-----------+ |
| | Trust Mgr |<--->|Telemetry A.| |
| +-----------+ +-----------+ |
+----------------|--------------------------+
|
v
+-------------------------------------------+
| Data Plane |
|-------------------------------------------|
| +------------+ +------------+ |
| | Sem.Router |<->| Sem.Router | |
| +------------+ +------------+ |
| ^ | ^ | |
| | | Telemetry | | |
| +---|-------------+---+ |
+-------------------------------------------+
Figure 1 The overall architecture for SAR
5. Functional Layers and Design Principles
SAR’s design is organized into five functional layers, each aligned
with a core principle that ensures scalability, intelligence, and
interoperability.
* Intent Layer: Generates and encodes Intent Vectors. Agents
describe their goals in structured form, including task types,
urgency, and context. The network uses these to infer optimal
paths and collaborators.
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* Identity and Trust Layer: Manages authentication, authorization,
and reputation. Each agent is bound to a unique identity
certificate, and trust scores are computed from telemetry.
* Policy Layer: The Policy Engine maps intents and trust data into
enforceable rules, determining which paths, nodes, or bandwidth
allocations are permitted.
* Semantic Routing Layer: Semantic Routers interpret policy rules
and update SFT entries dynamically based on trust or performance
metrics.
* Feedback Layer: Collects telemetry (e.g., latency, success rate,
anomaly detection) and continuously refines both trust and
policies.
SAR adheres to the following design principles:
* Semantic Composability: Each intent can be decomposed and
recombined, enabling fine-grained routing for multi-step agent
workflows.
* Trust Anchoring: Decisions are always contextualized by dynamic
trust values, preventing compromised agents from influencing
routing unfairly.
* Closed-Loop Adaptation: Every policy or path update is verified
through telemetry feedback, ensuring stable yet flexible routing
evolution.
* Interoperability: SAR MAY extend BGP, IS-IS, or gRPC metadata to
distribute semantic and trust information while maintaining
backward compatibility.
6. Control and Forwarding Procedures
SAR operates through coordinated procedures that integrate semantic
interpretation, trust evaluation, and routing execution. These
processes are logically divided between the control plane and
forwarding plane, yet are interconnected via telemetry and feedback.
1. Agent Registration: When an agent joins the network, it
authenticates with the Intent Controller (IC) and registers its
capabilities (e.g., compute type, model domain). The IC issues
credentials and a unique semantic prefix for the agent.
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2. Intent Submission: The agent generates an Intent Vector and
submits it to the IC. The Policy Engine (PE) parses the intent,
referencing domain policies to determine allowed routing
strategies.
3. Policy Translation: Based on the agent’s trust score and system
objectives, the PE compiles an executable rule set for the
Semantic Router (SR). These rules specify target domains,
quality preferences, and security constraints.
4. Routing Execution: SR uses its Semantic Forwarding Table (SFT) to
determine next hops. Forwarding is influenced by trust, latency,
and semantic relevance rather than just IP reachability.
5. Telemetry Feedback: The Telemetry Agent (TA) reports performance
data back to the Trust Manager (TM). Trust scores are
recalculated periodically, triggering policy adjustments when
thresholds are exceeded.
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+-----------+ +------------------+ +------------------+
| Agent | | Intent Controller| | Policy Engine |
+-----------+ +------------------+ +------------------+
| | |
| 1. Register/Authenticate| |
+------------------------>| |
| | |
| 2. Submit Intent Vector | |
+------------------------>| |
| | 3. Validate & Parse Intent|
| +-------------------------->|
| | |
| | 4. Generate Routing Policy|
| |<--------------------------+
| | |
| | 5. Install to Router |
| +-------------------------->|
| | |
| | 6. Ack/Confirm |
|<------------------------+ |
| | |
| 7. Data Forwarding via Semantic Routers |
|--------------------------------------------------->|
| | |
| 8. Telemetry Feedback |<--------------------------+
|<---------------------------------------------------|
| | |
| 9. Trust Update & Policy Adjustment |
+----------------------------------------------------+
Figure 2 The Workflow Overview for SAR
7. Conclusion
The SRA (Semantic Routing architecture) redefines how intelligent
systems communicate by integrating semantic intent, trust evaluation,
and adaptive policy control directly into the routing process. It
extends the traditional Internet model beyond topology and content
toward a truly intent-driven communication fabric that aligns network
behavior with the goals of autonomous AI agents. Through its layered
design—including intent processing, trust management, semantic
routing, and telemetry-driven feedback—SAR provides a coherent
framework capable of supporting large-scale, cross-domain AI
ecosystems with dynamic, secure, and efficient coordination.
Looking forward, several research and standardization opportunities
remain. First, common intent representation languages must be
defined to ensure interoperability among heterogeneous agents and
vendors. Second, mechanisms for distributed trust computation
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require standard metrics and synchronization protocols across
administrative domains. Third, integration of SAR with existing
Internet routing protocols such as BGP, IS-IS, or SRv6 will need
careful consideration to balance scalability with semantic
expressiveness. Finally, future work should investigate AI-assisted
optimization within the SAR control plane, enabling predictive policy
adjustments based on contextual learning.
In conclusion, SAR offers a foundational step toward an autonomous,
cognition-aware Internet, where the network itself participates in
decision-making, ensuring that communication among AI agents becomes
purposeful, trustworthy, and adaptive.
8. IANA Considerations
TBD
9. Acknowledgement
TBD
10. Normative References
[AIAgent] N, D., "Framework for AI Agent Networks draft-zyyhl-agent-
networks-framework-01", 2017.
[Istio] L, Larsson., "Impact of etcd deployment on kubernetes,
istio, and application performance", 2020.
[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>.
[ServiceMesh]
Li, W., "Service mesh: Challenges, state of the art, and
future research opportunities", 2019.
Authors' Addresses
Xueting Li
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: lixt2@foxmail.com
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Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: wangaj3@chinatelecom.cn
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