AgentxGCore: Agentic AI for Next-Generation Mobile Core Network

AgentxGCore proposes an AI-native architecture for next-generation mobile core networks (6G) using multi-agent systems that enable autonomous network optimization and management. The framework combines agentic AI with intent-based networking to replace centralized network management with self-organizing, self-adapting systems that leverage large language models for real-time decision-making.

AgentxGCore addresses a fundamental challenge in telecommunications infrastructure: how to manage increasingly complex networks at scale. As 6G development accelerates, traditional centralized network management becomes a bottleneck. The paper's innovation lies in applying multi-agent AI systems to network orchestration, creating specialized agents that plan and execute network operations autonomously based on user intents rather than explicit commands.

The integration of large language models into telecom infrastructure represents a paradigm shift. Rather than requiring engineers to manually configure networks for specific use cases, intent-based networking allows operators to specify desired outcomes—like "maximize throughput for video streaming"—and let AI agents determine optimal configurations. This mirrors broader enterprise automation trends where LLMs handle complex reasoning tasks.

For the telecommunications industry, this approach could significantly reduce operational costs and accelerate network optimization cycles. The closed-loop, self-adapting nature means networks respond to changing conditions in real-time without human intervention. This matters for infrastructure investors and telecom operators evaluating 6G rollout strategies. The open-source validation approach suggests the technology could democratize advanced network management beyond major carriers.

Key questions remain around AI reliability in mission-critical infrastructure and regulatory acceptance of autonomous network control. The success of AgentxGCore's multi-agent architecture in handling edge cases and failure scenarios will determine industry adoption. Future implementations must address explainability and safety constraints, especially as AI agents gain broader authority over network operations.

• →AgentxGCore enables autonomous 6G network management through specialized AI agents that replace centralized control systems.
• →Multi-agent architecture separates planning and execution functions, improving oversight and error correction in network operations.
• →Intent-based networking allows operators to specify business outcomes rather than technical configurations, reducing complexity.
• →Self-organizing, self-adapting networks can optimize performance in real-time based on live network conditions.
• →Open-source validation demonstrates feasibility but regulatory and safety frameworks for autonomous infrastructure control remain undeveloped.