The convergence of generative artificial intelligence and next-generation networks is giving rise to a new paradigm: agentic AI driven by large language models (LLMs). This approach transcends rule-based automation to enable autonomous, goal-oriented systems capable of managing, optimizing, and protecting 5G and 6G networks. In this tutorial and survey, we explore the foundations of agentic systems —reasoning, planning, tool use, and multi-agent coordination— and map them onto the control, management, and AI-native planes of advanced mobile networks. The analysis includes a technical and business perspective, highlighting how organizations can adopt these capabilities through AI solutions and automation services offered by Q2BSTUDIO.
The architecture of 5G/6G networks is evolving toward AI-native planes, where LLMs act as cognitive orchestrators. These agents not only process data but reason about the network state, plan corrective actions, and coordinate with other agents to ensure service quality. For example, an agent can detect a bottleneck in radio access, consult a knowledge base, decide to reallocate resources, and execute the reconfiguration via standardized APIs. This autonomous reasoning and execution capability requires robust support in cybersecurity, cloud computing, and real-time data analytics. Q2BSTUDIO, as a software development company, integrates these dimensions through custom applications that combine AI, cloud AWS/Azure, and cybersecurity.
Planning is another pillar of agentic AI. LLMs can decompose complex goals —such as minimizing latency in an edge network— into sub-objectives, select appropriate tools (simulators, databases, network APIs), and execute sequences of actions. Multi-agent coordination allows specialized agents to collaborate: one monitors traffic, another manages security, and a third optimizes energy consumption. This decentralized approach requires efficient communication protocols and trust mechanisms, which in turn rely on cloud infrastructures like AWS or Azure. For this reason, many companies are adopting cloud services to scale their AI agents and ensure high availability.
Evaluating agentic systems in 5G/6G networks presents unique challenges: not only decision accuracy must be measured, but also reaction speed, robustness against adversarial attacks, and adaptability to changing conditions. Traditional network performance metrics (throughput, latency, packet loss) are combined with quality-of-experience and agent reliability metrics. To address these challenges, Q2BSTUDIO offers Business Intelligence with Power BI services that enable real-time visualization and analysis of agent behavior and network status, integrating data from multiple sources.
Cybersecurity is a critical component. An AI agent controlling the network can be vulnerable to prompt poisoning, injection attacks, or training data manipulation. Therefore, it is essential to implement security layers that monitor agent actions, verify decisions, and protect communication channels. Q2BSTUDIO's cybersecurity solutions help companies audit and strengthen their agentic systems against emerging threats.
Standardization is another open front. Initiatives such as 3GPP, ETSI ENI, and the IETF are working on open interfaces that allow integration of AI agents with 5G and 6G control planes. Alignment with these standards is key for interoperability and large-scale deployment. Companies developing AI agents must consider these regulatory frameworks from the design stage.
From a business perspective, adopting agentic AI for 5G/6G networks offers significant competitive advantages: reduced operational costs, improved user experience, millisecond response to failures, and energy efficiency. However, it requires R&D investment and collaboration with technology partners that bring multidisciplinary expertise. Q2BSTUDIO, with its portfolio of services in automation, AI, cloud, and cybersecurity, positions itself as a strategic ally for organizations seeking to explore this new paradigm.
Open challenges are numerous: explainability of agent decisions, uncertainty management, data privacy, and energy sustainability of large models. Current research focuses on symbolic reasoning, reinforcement learning, and specialized language models for telecommunications domains. Combining these approaches with custom software platforms will enable the construction of autonomous, resilient, and efficient networks.
In conclusion, LLM-driven agentic AI represents a qualitative leap in 5G/6G network management. This tutorial and survey has provided a roadmap covering technical foundations, business implications, standardization, and future challenges. Companies that integrate these capabilities early, relying on providers like Q2BSTUDIO, will be better positioned to lead the next generation of autonomous telecommunications.
