Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking
Ruichen Zhang, Shunpu Tang, Yinqiu Liu, Dusit Niyato, Zehui Xiong,, Sumei Sun, Shiwen Mao, and Zhu Han

TL;DR
This paper explores how generative information retrieval techniques can enhance agentic AI for intelligent communications and networking, focusing on decision-making, knowledge management, and compliance with standards.
Contribution
It introduces an agentic contextual retrieval framework that integrates multi-source retrieval, reasoning, and validation to improve telecom-specific AI decision processes.
Findings
Framework improves answer accuracy and explanation consistency
Significantly enhances retrieval efficiency over traditional methods
Provides a comprehensive review of retrieval strategies in telecom AI
Abstract
The increasing complexity and scale of modern telecommunications networks demand intelligent automation to enhance efficiency, adaptability, and resilience. Agentic AI has emerged as a key paradigm for intelligent communications and networking, enabling AI-driven agents to perceive, reason, decide, and act within dynamic networking environments. However, effective decision-making in telecom applications, such as network planning, management, and resource allocation, requires integrating retrieval mechanisms that support multi-hop reasoning, historical cross-referencing, and compliance with evolving 3GPP standards. This article presents a forward-looking perspective on generative information retrieval-inspired intelligent communications and networking, emphasizing the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems. We first provide a…
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Taxonomy
TopicsCognitive Computing and Networks
