As someone working in technology, I believe no one in this field ever truly stops learning. Right now, I’m deep in my AI learning curve, and it’s absolutely fascinating — the technology itself, its implementation, and the operational impact it brings. I remember my first encounter with AI in telecom operations, specifically in analytics. AI-driven analytics provided valuable insights but lacked the autonomy to act on them. While AI could detect network anomalies, optimize resources, or flag potential issues, human intervention was still necessary to make decisions and execute actions. I think it almost feels like having a co-pilot who could identify turbulence ahead but couldn’t adjust the flight path. In a fast-paced telecom environment where real-time decision-making is critical — whether for network optimization, fault resolution, or customer engagement — this lack of autonomy led to inefficiencies, delays, and missed opportunities for proactive service enhancements.
Recently, I was introduced to Agentic AI and have been working closely with my Product Development team to explore its potential. Unlike traditional AI, Agentic AI is designed to not only analyze data but also make autonomous decisions and take proactive actions. This evolution in AI has the potential to redefine telecom operations by enhancing efficiency and reducing the need for human intervention in critical processes. In this blog, I want to discuss the broader adoption of this technology and the key differences between implementing traditional AI and transitioning to Agentic AI.
My Understanding of AI vs. Agentic AI
The conventional AI systems or traditional AI systems are designed to process vast amounts of data, recognize patterns, and provide insights. These systems often rely on predefined rules and require human intervention for decision-making and execution.
An example of traditional AI in telecom is AI-powered chatbots used for customer service. These chatbots can answer common queries, process simple requests, and route customers to the right department based on predefined rules. However, they lack true autonomy and adaptability — if a customer asks a question outside of their programmed responses, they either provide a generic answer or escalate the issue to a human agent. This reliance on predefined rules and human intervention highlights the limitations of traditional AI, which Agentic AI aims to overcome.
Agentic AI, is built on the foundation of autonomous decision-making. It proactively takes actions, learns from outcomes, adapts to new scenarios, and interacts dynamically with systems and users. It acts as an independent agent that can operate with minimal human input, making it a game-changer for industries like telecom.
Let me explain it better using the same Chatbot example, but using Agentic AI instead of traditional AI. Agentic AI would transform the chatbot by enabling it to go beyond predefined scripts and simple query handling. Instead of just responding to questions, an agentic AI-powered chatbot would understand customer intent, predict potential issues, and take proactive actions.
For instance, if a customer contacts support about slow internet speeds, a traditional AI chatbot might provide troubleshooting steps or escalate the case to a human agent. An agentic AI chatbot, however, could analyze network conditions in real time, detect congestion in the customer’s area, apply network optimizations autonomously, and then inform the customer about the resolution — without requiring human intervention. This level of autonomy enhances customer experience, reduces resolution time, and minimizes operational costs for telecom operators.
Does the Telecom Industry really need Agentic AI?
Well, the short answer is YES. Telecom networks are becoming increasingly complex, demanding real-time adaptability and intelligent automation. While traditional AI has already improved network management, customer service, and fraud detection, agentic AI takes it a step further by autonomously optimizing operations. It enables self-optimizing networks that dynamically adjust bandwidth, routing, and latency to enhance user experience and minimize downtime. As mentioned in an example earlier in this blog, unlike traditional chatbots that follow predefined scripts, agentic AI understands customer intent, anticipates issues, and delivers personalized solutions without human intervention. It also enables automated fault resolution by detecting and addressing network issues in real time, preventing failures before they impact service.
Additionally, agentic AI strengthens fraud prevention by continuously learning from evolving patterns and autonomously implementing security measures. Finally, it ensures intelligent resource allocation by dynamically distributing network capacity based on demand, delivering seamless service during peak times and special events.
What I think the future of Telecom with Agentic AI would look like.
We telecom peeps know that our industry is moving towards hyper-automation, therefore, agentic AI is going to be at the forefront of this transformation. With its ability to make context-aware decisions, self-learn, and operate independently, telecom operators can achieve unprecedented levels of efficiency, cost savings, and customer satisfaction.
In future blogs, I will delve into key components of Agentic AI architecture that contribute to its seamless efficiency, along with discussions on privacy, security, and other critical aspects as I continue exploring this technology. Until then, happy learning!
-Shivangi U Mohite (Senior Solutions Consultant/Product Manager)