The telecom industry is on the verge of a significant transformation, driven by advancements in artificial intelligence. At the forefront of this evolution is agentic AI, a form of artificial intelligence designed to reason and autonomously solve problems. This technology is increasingly being explored for applications ranging from customer service enhancements to network fault detection and resolution. Agentic AI’s integration into telecom operations was a hot topic at the 2025 Mobile World Congress (MWC), reflecting its growing relevance in the sector.
Understanding Agentic AI in Telecom
Agentic AI refers to an advanced form of AI that can execute complex tasks without requiring direct human intervention. Unlike large language models (LLMs), which typically handle text-based queries, agentic AI can interact with multiple smaller AI models or expert systems to achieve a desired outcome. This means AI agents can work autonomously or in collaboration with other AI models to address network inefficiencies, analyze performance issues, and provide real-time solutions.
Jürgen Hatheier, International CTO of Ciena, highlighted that agentic AI is not a single model performing multiple functions but a system that coordinates various AI capabilities to achieve a specific goal. Similarly, BT’s Chief Security and Networks Officer Howard Watson described it simply as “AI that does stuff,” emphasizing its ability to streamline operations without relying on large-scale AI models.
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How Agentic AI is Being Used in Telecom
Telecom companies are actively experimenting with agentic AI to optimize network performance and improve customer interactions. Some notable implementations include:
- Deutsche Telekom and Google Cloud Partnership: This collaboration utilizes agentic AI to analyze network behavior, detect performance issues, and implement corrections autonomously.
- Ericsson and Telenor’s AI-Powered Radio Control System: The system aims to reduce energy consumption at radio units by up to 4%, demonstrating AI’s potential for improving sustainability in telecom operations.
- Nokia’s AI Expansion: Nokia has introduced AI-driven security threat detection, service creation, and failure resolution, reinforcing its commitment to integrating AI into network management.
- Amdocs AI Implementation: Amdocs showcased an AI agent capable of diagnosing and addressing network latency issues by analyzing social media trends and user activity.
- Elisa Polystar’s AI Experimentation: Finnish telecom operator Elisa’s subsidiary, Polystar, is applying agentic AI to automation and problem resolution, expecting significant operational benefits in the near future.
The Challenges of Implementing Agentic AI
Despite its promising applications, agentic AI is still in its early stages. The technology faces several challenges, including:
1. Lack of Standardization
There is currently no universal definition of what qualifies as an AI agent, leading to inconsistencies in implementation and marketing claims. Amdocs Israel’s General Manager, Avishai Sharlin, noted that different companies define agentic AI in various ways, creating confusion about its capabilities and potential impact.
2. Varying Levels of Automation
Some telecom demonstrations at MWC suggested that AI agents were managing only basic tasks, while others showcased more sophisticated, multi-step reasoning processes. This disparity makes it difficult to gauge the true level of automation achieved.
3. Continuous Evolution
AI technology is evolving rapidly. AI solutions developed a year ago may already be outdated, making it challenging for telecom operators to keep pace with advancements. Steve Preston, CEO of Elisa Polystar, described the state of AI development as “a moving pool of mercury”—constantly shifting and difficult to pin down.
4. AI Hallucinations and Errors
AI-generated errors, often referred to as “hallucinations,” remain a concern. While AI reasoning models can be trained to minimize these mistakes, the potential for inaccuracies still exists. Companies like Totogi are addressing this by implementing rigorous quality checks and continuous training processes.
5. Workforce Displacement Concerns
The telecom industry has been experiencing workforce reductions in recent years. The integration of AI agents raises questions about whether further job cuts will occur as more tasks become automated. While AI can enhance efficiency, its impact on employment remains a significant concern.
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The Future of Agentic AI in Telecom
Despite these challenges, agentic AI holds immense promise for transforming telecom operations. As AI capabilities improve, we can expect:
- Enhanced Customer Support: AI-driven virtual assistants capable of resolving complex queries without human intervention.
- Improved Network Efficiency: AI agents that autonomously detect and fix network issues, reducing downtime and service disruptions.
- Optimized Resource Allocation: Intelligent systems that dynamically adjust network resources based on real-time demand.
- Increased Energy Efficiency: AI-powered solutions that minimize energy consumption, contributing to more sustainable telecom operations.
- Greater Security and Threat Detection: AI-driven security protocols capable of identifying and mitigating cyber threats in real time.
Conclusion
Agentic AI represents the next frontier in telecom innovation, offering unprecedented automation and efficiency. While its full potential is yet to be realized, ongoing advancements indicate a future where AI-driven solutions will play a crucial role in shaping the industry. However, companies must navigate challenges related to standardization, accuracy, workforce impact, and ethical considerations to fully harness the benefits of this transformative technology.
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Frequently Asked Questions (FAQs)
1. What is agentic AI?
Agentic AI is a form of artificial intelligence designed to perform tasks autonomously by interacting with multiple AI models or expert systems.
2. How is agentic AI different from generative AI?
While generative AI creates content, agentic AI is focused on executing actions and solving problems autonomously.
3. How is agentic AI used in telecom?
It is used for customer service automation, network fault detection, performance optimization, and security threat mitigation.
4. Which telecom companies are implementing agentic AI?
Companies like Deutsche Telekom, Google Cloud, Ericsson, Nokia, Amdocs, and Elisa Polystar are actively integrating agentic AI into their operations.
5. What are the benefits of agentic AI in telecom?
It improves efficiency, reduces operational costs, enhances customer experiences, and optimizes network performance.
6. What challenges does agentic AI face?
Challenges include lack of standardization, evolving technology, AI hallucinations, workforce impact, and security concerns.
7. Will agentic AI replace human jobs in telecom?
While AI can automate certain tasks, human oversight and expertise will still be necessary for complex decision-making processes.
8. How does agentic AI improve network security?
AI-powered security systems can detect threats in real time, prevent cyberattacks, and automate incident response.
9. Can agentic AI reduce telecom energy consumption?
Yes, AI-driven solutions can optimize power usage in network infrastructure, leading to energy savings and sustainability.
10. What is the future of agentic AI in telecom?
Agentic AI is expected to become more advanced, leading to increased automation, improved customer interactions, and enhanced network reliability.