Has Agentic AI Displaced Generative AI for Telcos?

Demystifying Generative, Predictive and Agentic AI

Generative AI (GenAI) creates content—from text and images to audio and code—using large language models (LLMs) and foundation models (FMs). It relies on patterns in existing data to generate new, contextually relevant outputs, whether public or private. Predictive AI, on the other hand, focuses on forecasting outcomes by analyzing historical and real-time data with machine learning models. It identifies anomalies, trends and forecasts future possibilities.

Agentic AI takes things a step further by autonomously executing complex, and often multi-step tasks to achieve a goal – with minimal human input. Unlike GenAI and Predictive AI, it isn't confined to a single technology—it adapts dynamically, leveraging any AI system and additional tools as needed to achieve its goals. This enables Agentic AI to navigate real-world scenarios, making informed decisions and executing those decisions.

When Does GenAI Become Agentic AI?

GenAI operates through LLMs like ChatGPT or Llama, creating content based on prompts from humans. While powerful, GenAI on its own is limited in telecom applications, as it has no access to the tools that allow it to make decisions and autonomously execute tasks. In this case, it remains non-agentic.

To transform GenAI into Agentic AI, companies like Netcracker have developed specialized platforms that create and run AI agents and give them access to tools to autonomously perform simple or complex tasks. These tools enable AI agents to inject real-time telco data into the LLM (securely) from a number of sources, for example:

• API function calls to BSS/OSS, analytics systems, knowledge graphs
• Knowledge bases (e.g FAQ, product configuration guides)
• Outputs from AI applications (e.g anomalies, forecasts)
• Code executors (e.g Python)

With this augmentation of GenAI, leveraging an Agentic AI framework, AI agents can make decisions and perform tasks autonomously - adding tremendous value to telecom. Examples include a Billing Agent that can resolve all sorts of questions related to a customer’s bill based on prior bill history;  a B2B CPQ Agent that integrate with CRM, ERP, and other enterprise systems to dynamically generate offers tailored to individual customers; and network AI agents that can analyze network performance and make real-time adjustments to ensure network quality is maintained.

Maximizing AI Impact: Multi-Agent Orchestration

However, the real value of agentic AI comes when AI agents work together to solve more complex tasks. By orchestrating multiple agents, telcos can offload more tasks to AI, giving it even higher levels of autonomy.   

Examples include:

• Enhancing Customer Experience With AI Agents: By interworking multiple agents, telcos can autonomously resolve inquiries, escalate issues and execute personalized service modifications. AI agents can intelligently optimize offers based on customer behavior, preferences and real-time demand shifts. With AI-powered engagement strategies, telcos can deliver hyper-personalized experiences that predict customer needs and proactively act on them.
• Improving Response to Network Incidents: Telcos can set into motion a number of activities in order to identify and resolve network anomalies through deep analysis of real-time network data across multiple domains and disparate systems. Ideally, a multi-agent approach can help prevent those outages in the first place by enabling digital twins that can predict future behavior based on analysis of network performance.
• Closing the Network Assurance Loop: Predictive AI is already improving fault detection and network optimization for many telcos, but the next step is to evolve to self-healing networks. Agentic AI enhances assurance by autonomously responding to alarms, pinpointing root causes and triggering corrective actions, eliminating manual troubleshooting delays. GenAI-based agents still play a crucial role by interpreting complex issues in natural language and activating AI-powered autonomous agents to take the necessary steps to activate fixes without human intervention.

After years of dominating AI conversations, GenAI has given way to the rise of Agentic AI in 2025. But the truth is, with the right tools, GenAI evolves into Agentic AI—enhancing autonomy and adaptability. The distinction is more about capability than category, proving that AI is only as powerful as the systems supporting it.