AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The presented project Learn2Organise addresses limitations in dynamic environments by developing an open-source architecture, called Lamas, that supports the creation and execution of learning, self-adaptive, and organisation-centred LLM-MAS.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
The analysis indicates that the promise of LLM-based agents for scalable automation, collaborative reasoning, and complex workflow execution comes with significant challenges in long-horizon reliability, evaluation standardization, communication security, cost-efficient orchestration, governance, and the interpretabili...
It seems that enterprise-wide multi-agent systems are a good building block for digital enterprises capable of adapting, scaling, and operating autonomously, on which future intelligent businesses would be able to operate.
A novel agent-defined architecture (ADA) is proposed to make the sixth-generation (6G) network a conversational and thinking network and supports intent-driven orchestration, reduces signaling latency, and keeps the additional resource overhead manageable under concurrent intent-processing loads.
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