A new era of intelligent automation is underway. We are seeing digital agents take on responsibilities that once required constant human supervision. Today, these systems, can make independent decisions, implement them, and continuously learn from their experiences.
Simply put, agentic AI listens, learns, and develops strategies capable of revolutionizing how we work, especially in network operations where it shifts from reactive to proactive, improving resilience and security. It can automate network management, real-time threat detection, and traffic optimization, enhancing efficiency, strengthening security, and boosting network performance for seamless and secure operations.
But how can it be implemented, where can it have the biggest benefit, what is the role of human oversight and what lessons can we learn from the introduction of agentic AI? In this article, I’ll cover these key points and give advice to businesses looking to harness its potential.
SVP of Product Engineering at GTT.
Competitive advantage through strategic agentic design
The successful implementation of agent-based AI systems requires careful planning.
Firstly, it is important to clearly define goals and key performance indicators for their use. Then, a major challenge is the seamless integration of the solutions into the existing IT network infrastructure. Training and operation of the systems also require the availability of sufficient and high-quality data. Finally, there are ethical considerations of implementing agentic AI that companies need to address from the outset, such as data privacy, protection, governance, human oversight and transparency, to ensure trust is built.
Agentic AI requires guidelines over which data it can access, from where, and whether it is able to share certain data externally. This is imperative to consider within an AI strategy to ensure that your customers and your organization are protected from data and regulatory breaches, such as the EU AI Act.
If your implementation plan takes these considerations into account, nothing stands in the way of the effective use of agentic AI. With digital agents, businesses can streamline their operations, meeting rising customer service expectations. A report by Gartner predicts that by 2029, AI will resolve 80% of common customer service issues without human intervention. These agents analyze customer sentiment in real time and provide tailored responses enhancing customer engagement.
Accelerated operations
Agentic AI is now playing a pivotal role in network infrastructure and cybersecurity, helping organizations move beyond traditional, rule-based systems. Unlike conventional tools that passively monitor and alert, digital agents can actively observe network behavior, identify anomalies in real time, and take autonomous action to resolve emerging threats. This enables a faster response to incidents, reducing downtime, and therefore helps avoid costly disruptions.
Agentic AI is already being embedded across networking and security infrastructure to deliver real-time, measurable value. The NSaaS model (Networking and Security as a Service) is evolving into something more dynamic, where agentic capabilities enable smart routing, adaptive policy enforcement, and predictive resource allocation. These enhancements ensure better performance, greater visibility, and stronger protection for global customers operating in complex conditions.
Meeting demand for adaptive, resilient solutions
There is growing demand for integrated cybersecurity and networking solutions from cloud providers, with many organizations viewing this convergence as essential to enterprise resilience. In this setting, agentic AI offers a unique advantage; it blends machine learning with autonomous decision-making, allowing digital agents to adapt in real time while maintaining stable and efficient network operations. This shift from static systems to intelligent, self-improving agents is reshaping how businesses think about their digital foundations. With this strategic mindset, early adoption of agentic AI gives network providers a chance to get ahead of the curve with smarter services, improved reliability, and a more personalized customer experience.
Unlocking new value
While we are still at the beginning of the AI journey and its potential is yet to be fully realized, McKinsey found that 77% of companies are either using AI or exploring its potential. It has already changed workflows, it still requires a level of human management, but agentic AI enables new possibilities. It can become more than a support tool. It can become an active participant in business operations, freeing up resources and creating greater efficiency. In networking specifically, the benefits are becoming clear. While machine learning has been used for tasks like digital twins and anomaly detection, agentic AI can manage these processes autonomously. This reduces the need for human intervention at every step and enables networks to become more resilient, secure, and adaptive to real-time demands.
Nevertheless, learning and development around AI in the workforce remains a business imperative. Counterintuitively, while flawed data is often tolerated in human decision-making, we don’t have the same leniency with AI. According to Gartner, 30% of generative AI projects are abandoned after the proof-of-concept stage, primarily due to issues related to data quality, risk management or high costs, highlighting the difficulty organizations face in AI initiatives and importance of getting implementation right from the off.
For some companies, agentic AI could mark a shift from promise to performance – where AI becomes not just an experiment, but a business-critical capability aligned to strategic goals.
Looking ahead
As businesses advance their AI capabilities, agentic systems offer a strategic step forward. They enable organizations to align automation with core objectives, turning networks into adaptive ecosystems. For organizations moving from passive AI and aiming to build intelligent, responsive systems, agentic AI is the key enabler. This is not just a technological shift, but a reimagining of what network infrastructure can achieve when paired with AI designed to think, act, and adapt.
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