



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
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   provided without warranty as described in the Revised BSD License.

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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