From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions

TL;DR

This paper proposes self-evolving agentic AI as a framework to enable autonomous, continual improvement of wireless systems. It articulates a layered architecture, end-to-end life cycle, and five self-evolving techniques (tool intelligence, workflow optimization, self-reflection, contextual adaptation, and evolutionary learning) and demonstrates them through a multi-agent cooperative system. The case study on LAWNs shows autonomous upgrading from fixed to movable antennas with significant beam-gain gains and recovery from degradation, achieved with minimal human intervention. The work highlights practical benefits for next-generation wireless intelligence, while outlining security, interoperability, and deployment considerations for real-world adoption.

Abstract

Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems by enabling autonomous agents to continually adapt and improve without human intervention. Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and workflows in response to environmental dynamics. This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques, including tool intelligence, workflow optimization, self-reflection, and evolutionary learning. We further propose a multi-agent cooperative self-evolving agentic AI framework, where multiple large language models (LLMs) are assigned role-specialized prompts under the coordination of a supervisor agent. Through structured dialogue, iterative feedback, and systematic validation, the system autonomously executes the entire life cycle without human intervention. A case study on antenna evolution in low-altitude wireless networks (LAWNs) demonstrates how the framework autonomously upgrades fixed antenna optimization into movable antenna optimization. Experimental results show that the proposed self-evolving agentic AI autonomously improves beam gain and restores degraded performance by up to 52.02%, consistently surpassing the fixed baseline with little to no human intervention and validating its adaptability and robustness for next-generation wireless intelligence.

Figures (4)

• Figure 1: Illustration of self-evolving agentic AI for intelligent wireless networks. Part A highlights the core components of agentic AI, including perception, knowledge and memory, reasoning and planning, and action and tooling. Part B illustrates the life cycle of agentic AI, showing how self-evolution is triggered when key performance indicators degrade or conditions shift. Part C presents the applications and evolution of agentic AI, where agents progress through iterative self-improvement to enhance detection, prediction, generation, and decision-making in wireless environments.
• Figure 2: Multi-agent cooperative self-evolving agentic AI framework for intelligent wireless networks. Part A illustrates a case study transitioning from fixed antenna optimization to movable antenna evolution in LAWNs. Part B presents the self-evolving pipeline, where multiple AI agents are orchestrated by a supervisor agent to support dynamic antenna evolution. Part C demonstrates a partial prompt example, showing how the supervisor agent leverages structured state variables and user instructions to guide the sequence of sub-agents.
• Figure 3: Collaborative interaction of LLM-driven agents. The monitoring agent detects performance degradation, and the supervisor invokes data collection and model selection agents, showing autonomous evolution without human intervention.
• Figure 4: Comparison of sum beam gain. Solid lines denote movable antennas, dashed lines denote fixed antennas, and hollow circles mark evolution.