Research on Key Technologies of Intelligent Agents and AI Plugins for Real-Time Communication Media Plane

An Wei; Wei Bin; Song Yue; Wang Chen; Zhang Lu

doi:10.11959/j.issn.1000-0801.DXKX260130

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Research on Key Technologies of Intelligent Agents and AI Plugins for Real-Time Communication Media Plane

更新时间：2026-06-24

- Research on Key Technologies of Intelligent Agents and AI Plugins for Real-Time Communication Media Plane
- Telecommunications Science (2026)
- 作者机构：
  
  1.中国移动通信有限公司研究院，北京 100053
  2.中兴通讯股份有限公司，广东深圳 518063
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260130
  CLC： TN915.81
- Received：28 February 2026，
  
  Revised：2026-05-07，
  
  Accepted：12 June 2026，
- 稿件说明：
移动端阅览
An Wei, Wei Bin, Song Yue, et al. Research on Key Technologies of Intelligent Agents and AI Plugins for Real-Time Communication Media Plane[J/OL]. Telecommunications Science, 2026.
DOI：

An Wei, Wei Bin, Song Yue, et al. Research on Key Technologies of Intelligent Agents and AI Plugins for Real-Time Communication Media Plane[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260130.

摘要

随着生成式人工智能快速发展，智能体作为新一代人机交互与自动化决策的核心，正逐步融入实时通信核心流程。然而智能体灵活多变的控制流与实时通信固定的媒体处理范式之间存在显著矛盾，制约了AI技术在实时通信场景中的深度应用。本文提出一种面向实时通信网络的融合媒体面与智能体协同调度架构，构建统一媒体框架（General Media Framework

GMF)，支持智能体以插件形式高效嵌入媒体处理流水线。针对媒体流高并发处理场景，提出内存零拷贝机制以压缩流处理时延；提出基于通用智能体的二级调度优化模型以消除多智能体并行带来的冗余计算。实验表明各类AI插件可稳定嵌入GMF流水线，所提方案满足实时通信时延要求，为实时通信网络的原生AI演进提供了理论基础和工程参考。

Abstract

With the rapid advancement of generative artificial intelligence

AI agents

as the core of next-generation human-computer interaction and automated decision-making

are gradually being integrated into the core processes of real-time communication. However

a fundamental conflict exists between the flexible control flow of agents and the fixed data processing patterns of the media plane

which severely restricts the in-depth application of AI technologies in real-time communication scenarios. To address this challenge

this paper proposes a plug-in and agent collaborative scheduling architecture for the converged media plane oriented towards real-time communication networks. A General Media Framework is constructed to support the efficient embedding of agents into the media pipelines in the form of plug-ins. For high-concurrency media stream processing scenarios

a zero-copy memory mechanism is proposed to significantly reduce latency during stream processing. Furthermore

a two-level scheduling optimization model based on a universal agent is presented to address the redundant computation problem caused by multiple agents running in parallel. Experimental results demonstrate that various AI plug-ins can be stably integrated into the GMF pipeline

and the proposed approach satisfies the end-to-end latency requirements of real-time communication

providing both a theoretical foundation and engineering reference for the AI-native evolution of real-time communication networks.

关键词

Keywords

references

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