Introduction to Agentic AI in 5G: Smarter Autonomous Networks

Learn how to engineer context-aware, agentic solutions. Build a foundation in Agentic AI, exploring core agent traits and the role of LLMs in autonomous decision-making. Trace mobile network evolution (1G to 5G) and dissect the "Agentic Loop." Discover how 5G integrates with AI frameworks, agents in OSS/BSS, strategic model use (specialized vs. general), and the synergy of generative and agentic AI for network operations. Gain practical insight into agentic design patterns and the five specialized roles of an agentic team for self-healing networks, automated configuration, and capacity forecasting. When you complete this course, you’ll have a thorough understanding of how to drive autonomous RAN optimization, predictive maintenance, and intelligent workflow automation, boosting efficiency and intelligence in the telecom landscape.

Learning objectives
Define Agentic AI and its core components, differentiating it from basic automation and generative AI, and explain the synergy between generative models and agentic frameworks in a telecom context.
Analyze the technological and market forces driving the convergence of AI and telecom, and map the Agentic Loop (Perceive → Reason → Act → Learn) onto real-world network operational challenges.
Compare traditional 5G network architectures with AI-integrated architectures, and evaluate how agentic systems interface with existing OSS/BSS and network functions to enable autonomous operations.
Design multi-agent systems by applying specialized agentic design patterns (e.g., Planner, Executor, Critic), and assess the cost-performance tradeoffs of using general versus specialized models for specific network tasks.
Critique the application of agentic systems in key telecom use cases, such as RAN optimization and predictive maintenance, and simulate an intelligent trouble ticket resolution workflow.
Formulate system design strategies for agentic solutions by applying core principles like context engineering and workflow decomposition, and justify model selection based on the specific requirements of a given network automation task.