AI has become so deeply embedded in our everyday working lives that it is no longer limited to data science teams or research labs. In telecoms, AI now plays a central role in network planning, optimisation, assurance and automation. As a result, the industry is rapidly absorbing a growing set of AI-related terms and concepts, many of which are directly relevant to how networks are evolving towards higher levels of autonomy.
I recently came across the video embedded below, which provides clear explanations of seven AI terms that are becoming increasingly important in the context of network intelligence and autonomous networks. Some of these concepts are already being applied in operational networks today, while others point clearly towards the direction of travel for AI-native 5G Advanced and 6G systems.
The video begins with Agentic AI, a concept that aligns closely with the telecom industry’s vision for autonomous networks as defined in 3GPP. Unlike traditional AI models that respond to a single prompt, AI agents can perceive their environment, reason about next steps, take action and observe the outcome in a continuous loop. In practical terms, this maps well to closed-loop automation use cases such as self-healing, energy optimisation, dynamic resource allocation and intent-driven network management.
Closely related are Large Reasoning Models, which are designed to work through problems step by step rather than producing an immediate response. This capability is particularly relevant for telecom networks, where decisions often span multiple domains, layers and vendors. As AI systems take on greater responsibility for operational decisions, reasoning-based models become essential for safe and explainable automation.
The video then moves to more foundational enablers, starting with Vector Databases. Telecom networks generate vast volumes of unstructured data, including logs, alarms, performance metrics, configuration data and documentation. Vector databases allow this information to be searched and correlated based on semantic meaning rather than simple keywords, enabling more context-aware and intelligent AI systems.
This naturally leads to Retrieval-Augmented Generation (RAG), which is already gaining traction in telecom operations. By combining large language models with operator-specific data sources such as standards, network documentation or operational procedures, RAG helps ground AI outputs in trusted information. This is particularly important in network operations, where accuracy and reliability are critical.
Another important concept discussed is the Model Context Protocol (MCP), which addresses how AI models interact with external tools and systems. For telecom operators, standardised mechanisms for AI access to network management systems, data platforms and orchestration tools could significantly simplify integration and accelerate the deployment of AI-driven automation across the network lifecycle.
The video also touches on Mixture of Experts (MoE) models, which provide a more efficient way to scale AI by activating only the parts of a model needed for a specific task. This approach is especially relevant for telecom use cases where compute efficiency, latency and energy consumption are key constraints, particularly as AI capabilities move closer to the edge of the network.
Finally, the video briefly discusses Artificial Superintelligence (ASI). While ASI remains theoretical, it is often referenced in long-term discussions around AI evolution. For the telecom industry, it serves as a reminder of the rapid pace of change and the importance of governance, trust and control as networks become increasingly autonomous and software-driven.
Overall, this video offers a useful technical refresher on AI concepts that are already shaping the development of network intelligence, autonomous operations and AI-native architectures. For anyone working on 5G Advanced, autonomous networks or early 6G thinking, these are terms that are quickly becoming part of the industry’s everyday vocabulary.
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