Agentic AI: Autonomous Intelligence for the future telecom networks
Problem Statement:
Agentic networks, composed of autonomous agents that interact and make decisions, are transforming various fields by enhancing efficiency, security, and adaptability. In telecom and IoT applications, these networks enable dynamic resource allocation, predictive maintenance, and robust security measures. For instance, in smart grids, agentic networks balance energy supply and demand, while in telecom, they optimize network performance and reduce downtime through proactive maintenance. By leveraging advanced algorithms like multi-agent reinforcement learning, agentic networks can effectively manage complex systems, ensuring reliable and efficient operations even in dynamic and adversarial environments. But how do these agents agree on a shared policy? What do they communicate with each other?
Let’s have a look at agentic networks in different fields:
- Network Operations and Management:
- Telecom Network Optimization: Companies like SoftBank and Tech Mahindra are using agentic AI to develop large telco models (LTMs) and AI agents that automate complex decision-making workflows, improve operational efficiency, and enhance network performance. These AI agents can predict network failures, automate resolutions, and optimize network configurations, leading to reduced downtime and improved customer experiences.
- IoT Device Management:
- Smart Home Automation: Agentic networks are used in smart home systems to manage and optimize the interactions between various IoT devices. Autonomous agents can control lighting, heating, security systems, and appliances based on user preferences and environmental conditions. Industrial IoT: In manufacturing, agentic networks manage IoT devices to monitor equipment health, predict maintenance needs, and optimize production processes. This leads to increased efficiency, reduced downtime, and improved safety.
- Security and Surveillance:
- Traffic Surveillance: UAV swarms equipped with agentic AI can perform collaborative target tracking and traffic surveillance. These autonomous agents share observations and learning parameters to enhance the accuracy and efficiency of surveillance operations. Disaster Response: Agentic networks are used in disaster management to coordinate rescue operations. Autonomous agents can represent different emergency services, working together to allocate resources and respond to incidents efficiently.
- Resource Allocation:
- Edge Computing: Multi-agent reinforcement learning (MARL) is used in edge computing to optimize resource allocation. Agents can dynamically allocate computational resources based on real-time demands, improving the performance and efficiency of edge devices. Energy Management: In smart grids, agentic networks balance supply and demand by coordinating various energy sources and consumers. Autonomous agents optimize energy distribution, reduce waste, and enhance grid stability.
- Predictive Maintenance:
- Telecom Infrastructure: Agentic AI is used to monitor telecom infrastructure, predict equipment failures, and schedule maintenance activities. This proactive approach reduces downtime and maintenance costs. IoT Sensors: In IoT networks, autonomous agents analyze sensor data to predict when devices need maintenance or replacement, ensuring continuous operation and reducing unexpected failures.
Tasks:
You can choose a research question from above (or propose yours!). Then it’s time to model and study the interaction between agents, you can use GAMA, NETLOGO, Repast, AUTOGEN or any other agentic framework you like! Build your world!
Work:
10% Theory, 70% Simulations, 20%Writing
Contact:
Alessandro Chiumento (a.chiumento@utwente.nl)