1. What is Agentic AI

We are entering a new era in how software is created and integrated, driven by advancements in AI that combine generative logic with the ability to plan and execute tasks autonomously. This emerging field, known as Agentic AI, represents a significant evolution in artificial intelligence, enabling systems that can not only process and analyze information but also take proactive actions based on objectives, feedback, and changing conditions. For the telco industry, Agentic AI offers a unique opportunity to tackle the increasing complexity of networks, applications, and regulatory requirements by introducing intelligent, autonomous solutions that redefine how operations are managed and optimized.

Even the world’s leading tech leaders recognize the profound implications of this shift. Microsoft CEO Satya Nadella envisions a future where "humans and swarms of AI agents will be the next frontier," (see Nasdaq) while Salesforce CEO Marc Benioff describes Agentic AI as a "new labor model, new productivity model, and a new economic model." (see fortune.com). These perspectives highlight the transformative potential of Agentic AI as a smarter and more dynamic approach to automating business processes.  

Agentic AI is a combination of generative AI (large language models), decision loops, memory, and automation. They are autonomous entities capable of reasoning, taking action, observing outcomes, and adapting their actions accordingly. Their ability to think and make decisions natively sets them apart from current automation approaches, driving a significant leap forward in innovation and capability. It is more than a technological breakthrough—it is a paradigm shift, transforming industries from reactive systems to proactive, intelligent ecosystems. In telecom, this could mean a revolution in business and network operations, where complexity becomes manageable, scalability is seamless, and opportunities for optimization and innovation abound.

This article explores the disruption potential of Agentic AI in telecom, focusing on how it can unlock network operational efficiencies, improve customer experiences, and enable networks to thrive in an era of rising complexity and security risks.

2. Agentic AI is a Game Changer for Telco Operations
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1. From Reactive to Proactive Operations: Current network operations rely heavily on monitoring tools and human intervention to respond to issues as they arise. Agentic AI enables a shift to proactive and autonomous operations, where AI agents adapt to evolving situations, diagnose, and resolve network issues without waiting for human input. A key aspect of Agentic AI is its capability to dynamically adjust to changing conditions. Unlike traditional predefined workflows, Agentic workflows leverage real-time data, predictive models, and self-directed decision-making loops to determine the right actions in any given context. This represents a fundamental departure from static workflows, offering more flexible and resilient network management.

Use Case #1: Instead of waiting for alarms from service management systems, Agentic AI identifies potential service degradations before they occur, determines root causes, and applies solutions autonomously.

2. Scalability Beyond Human Capacity: Telco networks are becoming increasingly complex, with 5G, IoT, edge computing, and virtualization generating vast volumes of data and a multitude of dependencies. Human engineers cannot scale to handle this complexity effectively, and traditional tools are limited by predefined logic. Agentic AI, with its ability to act autonomously across RAN, Core, IMS, Transport, and Services layers of the network, can scale decision-making and execution to match network complexity.

Use Case #2:  Managing millions of IoT devices connected to a telco network requires dynamic resource allocation, which Agentic AI achieves by adapting seamlessly to changing network conditions. Agentic AI enables more effective and responsive resource management by continuously adjusting to the evolving demands of the network.

3. Autonomous Closed-Loop Systems: The promise of closed-loop automation has long been a goal in telco operations, but current implementations often require manual oversight or intervention to finalize actions. Agentic AI integrates autonomous closed-loop systems that monitor, analyze, plan, execute and validate changes in real-time, reducing operational overhead and the need for human validation.

Use Case #3: In self-healing networks, Agentic AI can detect packet loss, packet delay, and jitter caused by a misconfigured route, radio antenna or others, reconfigure the network functions autonomously, and confirm service restoration without waiting for a technician.

4. Adaptability in Network Evolution: The telecom landscape is rapidly evolving, with new technologies and use cases, e.g. private 5G, network slicing, API monetization APIs 5G Monetization, demanding flexibility. Agentic AI offers a new paradigm of adaptability, where agents can continuously learn from real-world data and refine their strategies, unlike static automation scripts or predefined workflows.

Use Case #4:  In network slicing, Agentic AI can dynamically create and manage slices in response to changing application requirements, such as supporting low-latency services for AR/VR. Agentic AI facilitates efficient resource allocation and responsive adjustments to meet evolving demands.

5. Cost Management Through Intelligent Resource Utilization: Telcos often struggle with inefficient resource management and high operational costs. Agentic AI supports intelligent resource utilization by identifying underutilized assets, reallocating resources dynamically, and automating maintenance schedules. Its strength lies in monitoring, adaptive management, and making real-time decisions to enhance resource efficiency.

Use Case #5: AI agents can monitor energy usage in radio units, dormant cells and dynamically adjust power levels or schedule downtime during low-traffic periods, reducing operational costs.

3. Agentic AI is a Shift in the Foundation of Telco Operations
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1. If implemented narrowly, Agentic AI could risk being perceived as incremental—a more advanced form of automation or monitoring. However, its true game-changing potential lies in redefining the role of humans in network operations and enabling a fundamentally new operational paradigm:

• Humans as Strategists, Not Executors: Engineers shift from troubleshooting and executing tasks to setting high-level objectives and monitoring outcomes. Agentic AI handles the execution.

• Self-Optimizing Networks: Networks become self-sufficient, dynamically adjusting configurations and policies in real-time without predefined rules.

• Intelligence in Every Layer: From the transport layer—configuring routes, to the radio layer—optimizing radio frequency and tilt parameters, to core and service layer—defining policy control and charging rules, quality of service indicators, and services, Agentic AI acts as an autonomous orchestrated multi-agent ecosystem, removing silos.

2. Agentic AI could also usher in intent-based operations, where operators define what outcomes they need, and the AI determines how to achieve and validate them. This approach removes the need for detailed operational logic and manual validation by:

• Combining intent-driven frameworks from operators with continuous learning.

• Allowing the network to evolve in response to new demands or technologies with minimal intervention.

• Enhancing resilience by ensuring the network can self-heal and adapt to unpredictable conditions.

3. Agentic AI could succeed where others have struggled by its adaptative, flexible and decentralized architecture, and integrated automation capabilities such as:

• Real-Time Adaptation: Unlike static tools, Agentic AI continuously learns and refines its approach, ensuring it remains effective in dynamic environments.

• Decentralized Decision-Making: Agents operate independently but cohesively, avoiding bottlenecks that slow traditional systems.

• Lower Barriers to Automation: By combining decision-making, execution, and validation, Agentic AI can handle open-ended problems that current automation tools cannot.

4. Key Enablers Required for Agentic AI in Telco Operations  

While Agentic AI holds immense potential, it would be overly optimistic to assume it can solve all operational challenges. Instead, it should be applied judiciously, particularly to address open-ended problems requiring adaptability, tasks that scale with complexity, and environments with trusted autonomy supported by deterministic feedback loops. Below are the key enablers necessary for integrating Agentic AI into telco operations:

1. Availability of Network Procedures and Knowledge: Internal network procedures and relevant data must be made accessible to serve as the foundational knowledge base. This enables AI agents to effectively adapt and make informed decisions during their initial training and deployment phases.

2. Exposure to Network APIs: Access to network function APIs is critical to ensure that the actions taken by AI agents are accurate, verifiable, reversible, and idempotent. Projects like The Linux Foundation's Camara and IEEE Camara Testbeds provide frameworks to standardize and expose these APIs effectively.

3. Secure Access to Advanced AI/ML Frameworks: Access to cutting-edge AI/ML frameworks and infrastructures is essential to ensure speed, precision, and the ability to experiment safely. This minimizes risks and maximizes performance when deploying Agentic AI in live environments.

4. Strategic Partnerships: Collaborations with the right partners are crucial for co-creating and implementing Agentic AI solutions. A phased approach—starting small, proving value, learning iteratively, and scaling horizontally and vertically—will ensure sustainable growth and impact.

Conclusion: A Leap, not a Step

Agentic AI is not just a better technology; it represents a leap in how telcos operate. By transitioning from human-driven processes to intent-based, autonomous systems, telcos can address the complexities of modern networks, reduce costs, and deliver superior customer experiences. The game-changer lies in this ability to transform network operations into a fully autonomous, intelligent ecosystem, setting the foundation for the next generation of telecom innovation.

Let’s explore how to co-innovate with Agentic AI in your network.

Join roundtable discussions at MWC for deeper insights and highlight how B-Yond is paving the way for autonomous networks.

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