Agent Communication Gateway for Semantic Routing and Working Memory
draft-agent-gw-00
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draft-agent-gw-00
Agent-GW Xiaohui. Xie
Internet-Draft Tsinghua University
Intended status: Standards Track Zian. Wang
Expires: 20 August 2026Beijing University of Posts and Telecommunications
Tianshuo. Hu
Tsinghua University
16 February 2026
Agent Communication Gateway for Semantic Routing and Working Memory
draft-agent-gw-00
Abstract
This document presents an architectural framework for an Intelligent
Agent Communication Gateway (Agent-GW), designed to support large-
scale, heterogeneous, and dynamic multi-agent collaboration. As
agents evolve from isolated software entities to a collaborative
digital workforce, the underlying infrastructure must transition from
rigid, host-based connectivity to flexible, intent-based interaction.
This document outlines the requirements for such a transition and
proposes the Agent-GW as a unified infrastructure hub. The gateway
provides native primitives for Semantic Routing—dispatching tasks
based on intent and capability—and Working Memory, which manages
structured context for multi-step workflows. Furthermore, it defines
mechanisms for automated protocol adaptation, oracle-free agent
evaluation, and collaborative inference acceleration (KDN). The
architecture aims to enable agents and legacy systems to interoperate
through standardized protocols while ensuring observability,
security, and operational scalability.
Status of This Memo
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This Internet-Draft will expire on 20 August 2026.
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Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 3
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. Network and Infrastructure Requirements . . . . . . . . . . . 4
5. Architecture Overview . . . . . . . . . . . . . . . . . . . . 4
5.1. Architectural Model . . . . . . . . . . . . . . . . . . . 4
6. Infrastructure Functions Enabling Active Network
Participation . . . . . . . . . . . . . . . . . . . . . . 5
6.1. Agent Identification and Capability Directory . . . . . . 6
6.2. Automated Protocol Adaptation and Interface
Normalization . . . . . . . . . . . . . . . . . . . . . . 6
6.3. Infrastructure-Level Agent Evaluation and Compliance . . 6
6.4. Dynamic Orchestration and Semantic Routing Mechanism . . 7
6.5. Evolutionary Knowledge Management . . . . . . . . . . . . 7
6.6. Collaborative Inference Acceleration (KDN) . . . . . . . 7
7. Security Considerations . . . . . . . . . . . . . . . . . . . 7
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 8
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
10.1. Normative References . . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 8
1. Introduction
The rapid advancement of Large Language Models (LLMs) has catalyzed
the emergence of the "Internet of Agents," a paradigm where
autonomous software entities and tool-like services interconnect to
form collaborative workflows. Unlike traditional microservices,
these agents possess varying degrees of autonomy, reasoning
capabilities, and diverse interface standards. Early agent
deployments were typically siloed within proprietary frameworks,
limiting their ability to collaborate across administrative domains
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or heterogeneous platforms.
As these systems scale, the fundamental bottleneck shifts from basic
network connectivity to context management and efficient
orchestration. Delivering the right context to the right agent at
the right time—while managing the high computational cost of
inference—becomes a critical infrastructure challenge. Existing
network/application gateways, designed for static endpoints and
stateless packet forwarding, lack the semantic awareness required to
interpret agent intents or manage the lifecycle of a collaborative
task.
This document introduces the Agent Communication Gateway (Agent-GW),
an architectural entity situated between agents and external tools or
services. The Agent-GW elevates the network's role from a passive
transport layer to an active semantic intermediary. It introduces
two core primitives: Semantic Routing, which decouples task execution
from physical endpoints by routing based on capabilities and runtime
state; and Working Memory, which provides a shared, incrementally
updated context layer to support multi-step reasoning.
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:
Agent-GW (Agent-GW) Agent Communication Gateway; the infrastructure
component coordinating multi-agent communication, responsible for
protocol translation, semantic routing, and context management.
Semantic Routing The process of routing a request based on the
semantic intent of the task and the capabilities of available
agents.
Working Memory A structured, temporary storage mechanism within the
gateway that maintains the context and state of a multi-turn agent
interaction.
KDN (Knowledge Delivery Network) A mechanism that treats inference
states (e.g., LLM KV caches) as reusable and distributable
artifacts.
MCP Model Context Protocol; a reference standard for connecting AI
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assistants to systems/data.
4. Network and Infrastructure Requirements
The proliferation of intelligent agents fundamentally reshapes
interaction patterns in future networks. Agent interactions are
typically short-lived, context-heavy, and driven by high-level goals
rather than explicit commands. To support this, the infrastructure
must satisfy the following requirements:
*Intent-Based Addressing:* The network must support addressing
schemes based on what constitutes the service (Capability) rather
than where it is located (Topology).
*Stateful Context Management:* Unlike stateless HTTP requests,
agentic workflows often involve multi-turn reasoning where context
accumulates.
*Heterogeneous Interoperability:* The ecosystem comprises diverse
entities. The infrastructure must provide automated adaptation
layers.
*Dynamic Capability Discovery:* The network requires a dynamic
discovery mechanism that can match task needs with agent capabilities
in real-time.
*Inference Efficiency:* Mechanisms to cache and share intermediate
inference states (such as KV caches) are required.
5. Architecture Overview
This section describes the reference architecture of the Agent
Communication Gateway (Agent-GW). It functions as a Semantic
Intermediary operating at the application and cognitive layers.
5.1. Architectural Model
Figure 1 illustrates the logical components and their interactions
within the Agent-GW.
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[ Northbound: User / Client Agents ]
|
+------------------------------------------+------------------------------------------+
| 1. Access & Adaptation Plane (Ingress) |
| +-----------------------+ +-------------------------+ +-----------------------+ |
| | Protocol Sniffer | | Auto-Adapter Engine | | Trust Enforcer | |
| | (Transport Detection) |-->| (Schema -> MCP Transform)|-->| (Auth / Sandboxing) | |
| +-----------------------+ +-------------------------+ +-----------------------+ |
+------------------------------------------+------------------------------------------+
| Normalized Semantic Request
+------------------------------------------v------------------------------------------+
| 2. Cognitive Orchestration Plane (Control) |
| +---------------------------------------------------------------------------------+ |
| | Semantic Router & Planner | |
| | +------------------+ +------------------------+ +---------------------+ | |
| | | Intent Parser |---->| Task DAG Decomposer |---->| Dynamic Dispatcher | | |
| | +------------------+ +------------------------+ +---------------------+ | |
| +----------------------------------------+----------------------------------------+ |
| | |
| +-------------------------------+-------------------------------+ |
| v (Read/Write) v (Cache Hit) v (Sync) |
+----------+-------------------------------+-------------------------------+----------+
| 3. Knowledge & State Plane (Data & Compute) |
| +-----------------------+ +---------------------------+ +---------------------+ |
| | Working Memory | | Evolutionary Memory | | KDN Accelerator | |
| | (Session Context) | | (Experience/Feedback) | | (KV Cache Sharing) | |
| +-----------------------+ +---------------------------+ +---------------------+ |
+------------------------------------------+------------------------------------------+
| Executable Actions / Payloads
+------------------------------------------v------------------------------------------+
| 4. Ecosystem Interface Plane (Egress/Southbound) |
| +---------------------------------------------------------------------------------+ |
| | Unified Driver Layer | |
| +-----------+--------------------+---------------------+--------------------+-----+ |
| | | | | |
| v v v v |
| +------------------+ +------------------+ +------------------+ +-------------+ |
| | Legacy Systems | | Native Agents | | Physical World | | Peer Mesh | |
| | (REST/RPC Wrappers)| | (MCP Servers) | | (ROS/IoT Bridge) | | (Agent-GW Sync) | |
| +------------------+ +------------------+ +------------------+ +-------------+ |
+-------------------------------------------------------------------------------------+
Figure 1: Agent-GW Reference Architecture
6. Infrastructure Functions Enabling Active Network Participation
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6.1. Agent Identification and Capability Directory
This function establishes the "Root of Trust" for the agent network,
shifting security from network-layer spoofing prevention to
application-layer Capability Spoofing mitigation. The Agent-GW
maintains a dynamic, verified directory where agent entries are not
static records but active, verified states.
*Cryptographic Identity and Verification:* Participating agents MUST
possess a Cryptographic Agent ID (AID) derived from an X.509v3
digital certificate. Upon registration, the agent submits an
AgentCard binding its identity to a specific capability hash. To
prevent the registration of malicious entities, the Agent-GW
implements a Capability Claim and Verification (CCV) mechanism.
Utilizing Metamorphic Testing principles, the gateway issues
"Challenge-Response" queries (e.g., semantic variants of a task) to
verify the agent's functional consistency without accessing its
internal model weights (Zero-Knowledge verification).
*Semantic Heartbeat and Dynamic Pruning:* To maintain directory
freshness, the Agent-GW enforces a Semantic Heartbeat. Unlike
traditional Layer 3 keep-alives that only confirm network
reachability, this mechanism periodically verifies Layer 7 functional
integrity. Agents that fail these semantic challenges (indicating
they are "Zombie Agents" or functionally impaired) are dynamically
pruned from the directory.
6.2. Automated Protocol Adaptation and Interface Normalization
Residing within the Access & Adaptation Plane, this function serves
as the "Semantic Edge" that normalizes heterogeneous external
protocols (e.g., HTTP, MQTT, gRPC) into the unified Model Context
Protocol (MCP) used by the internal Orchestration Plane.
To handle unstructured or poorly documented interfaces, the Agent-GW
implements a Generative Adaptation Mechanism with Active Probing.
Instead of relying on static drivers, an LLM-based engine ingests raw
interface descriptions to generate preliminary bindings. These
bindings are iteratively refined through a self correcting feedback
loop.
6.3. Infrastructure-Level Agent Evaluation and Compliance
Agents are often deployed as "black-box" entities where internal
logic is opaque. The Agent-GW introduces an infrastructure-level
evaluation mechanism to ensure reliability and compliance without
requiring access to model weights.
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This function employs Metamorphic Testing protocols: the gateway
generates semantic variations of task instructions (e.g., rewriting
the prompt or injecting noise) and evaluates the consistency of the
agent's responses. This "Oracle-free" approach allows the gateway to
assign a dynamic reliability score to each agent.
6.4. Dynamic Orchestration and Semantic Routing Mechanism
Static routing tables are insufficient for dynamic multi-agent
collaboration. The Agent-GW implements Semantic Routing, a mechanism
that dispatches tasks based on high-level intent, real-time
capability matching, and operational constraints.
The Cognitive Orchestration Plane decomposes complex user intents
into a Directed Acyclic Graph (DAG) of sub-tasks. The Dynamic
Dispatcher then assigns these sub-tasks to the most suitable agents
based on Capability Match, Trust Score, and Operational Metrics.
6.5. Evolutionary Knowledge Management
To improve collaboration efficiency over time, the Agent-GW
incorporates Evolutionary Memory. This function transforms the
gateway from a stateless forwarder into a learning infrastructure.
The gateway captures execution traces, success/failure feedback, and
user corrections from passing traffic. In local or edge deployments,
this allows the Agent-GW to build a localized knowledge base to
refine routing policies and provide "Feedback Guidance" to terminal
agents.
6.6. Collaborative Inference Acceleration (KDN)
Multi-agent workflows frequently involve redundant reasoning over
shared contexts. To address the computational inefficiency, the
architecture proposes a Knowledge Delivery Network (KDN).
The KDN function enables the sharing of intermediate inference
states, specifically the Key-Value (KV) cache of the LLM, across co-
located agents or peer gateways. This significantly reduces the
Time-to-First-Token (TTFT) and overall computational load.
7. Security Considerations
The introduction of an active Agent-GW introduces specific security
challenges: Agent Identity Spoofing, Capability Poisoning, Context
Leakage, and Inference Artifact Security.
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8. IANA Considerations
This document has no IANA actions at this time.
9. Acknowledgement
TBD
10. References
10.1. Normative References
[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>.
Authors' Addresses
Xiaohui Xie
Tsinghua University
Email: xiexiaohui@tsinghua.edu.cn
Zian Wang
Beijing University of Posts and Telecommunications
Email: zianwang@bupt.edu.cn
Tianshuo Hu
Tsinghua University
Email: huts22@mails.tsinghua.edu.cn
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