Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking

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 comprehensive review of generative information retrieval strategies, including traditional retrieval, hybrid retrieval, semantic retrieval, knowledge-based retrieval, and agentic contextual retrieval. We then analyze their advantages, limitations, and suitability for various networking scenarios. Next, we present a survey about their applications in communications and networking. Additionally, we introduce an agentic contextual retrieval framework to enhance telecom-specific planning by integrating multi-source retrieval, structured reasoning, and self-reflective validation. Experimental results demonstrate that our framework significantly improves answer accuracy, explanation consistency, and retrieval efficiency compared to traditional and semantic retrieval methods. Finally, we outline future research directions.