The Network is Better with Agentic AI

Managing and maintaining technology networks can be an expensive business. One estimate says that U.S. organizations are currently spending about $100 per employee on enterprise network infrastructure. But cost is not the only concern. Companies have to grapple with issues, such as network complexity, security threats, disruption risk, new technology integration, and skills shortage, among others.

But that may become a thing of the past thanks to agentic AI. Leading the next wave of AI innovation, agentic (or autonomous) AI is capable of ingesting huge data volumes in real-time to learn, adapt, solve complex multi-step problems, and even autonomously perform tasks on behalf of users. Whereas traditional AI needs predefined rules to function, agentic AI is self-operating, proactive, goal-oriented and capable of taking decisions on its own.
These virtues could transform enterprise network operations by doing the following:

Enabling Autonomous Network Management

Agentic AI creates autonomous networks that can manage, optimize and heal themselves with little or no human intervention. These networks don’t just automate routine operations, such as configuration, monitoring and maintenance to increase efficiency, they also adjust to changing conditions in real-time, identify issues proactively, and trigger corrective measures to resolve problems quickly, reduce downtime and improve network reliability. By automating workflows, autonomous networks not only save costs but also mitigate skill shortages by freeing up network maintenance staff to focus on value-adding tasks.

Optimizing Networks in Real-Time

Agentic AI can anticipate network problems based on historical and current network data to enable proactive, and even autonomous, maintenance. What’s more, by analyzing information, such as network traffic and resource usage patterns, agentic AI dynamically adjusts network parameters to improve performance and resource usage in real-time. Upon identifying bottlenecks and other issues potentially leading to failure, autonomous agents take suitable action, such as rerouting traffic and addressing the problems before they disrupt the network, to ensure service reliability. Self-healing properties detect and fix outages or decline in performance without human involvement.

Boosting Network Security

When autonomous agents detect suspicious network activity or confirmed threats, they immediately trigger security protocols and actions – for example, isolating compromised devices – to contain the risks and protect the network from attack.

Providing Dynamic Scalability

Agentic AI tools automatically ramp up or scale back network resources based on demand to sustain performance even at peak load or prevent wastage of resources during quiet times, as the case may be. Apart from allocating bandwidth and adjusting server capacity based on real-time demand, agentic AI adapts easily to different network conditions. Scalability and adaptability make agentic AI a versatile solution that can be used across enterprise networks of varying size and complexity.

Enhancing User Experience

Autonomous agents can deploy their learning from data, such as user behavior and network conditions, to personalise customer experience. For example, they can dynamically modify network parameters to ensure every user enjoys seamless communication and entertainment. Even network management interactions are frictionless because instead of bothering with manual configurations, users can simply describe the desired outcome in their own words, and agentic AI will understand and act upon the request.

Highly versatile and capable, agentic AI finds application in a wide range of network operations. For the best results, organisations should deploy the technology across the network lifecycle – real-time network planning and design; real-time network build; real-time network change management using agentic AI-enabled digital-twins; dynamic network service provisioning; closed-loop assurance and zero-touch operations. Further, they should ensure that development and deployment happen in conformance to responsible AI principles. There should be adequate guardrails to ensure agentic AI systems operate within the boundaries of data security and privacy regulations. Efforts should be made to improve algorithmic transparency and explainability and avoid bias in decision-making. Strong AI governance, federated fairness audits, and human oversight of critical operations would help to create agents that are not only autonomous but also ethical and human-centric.

-By Manjunath DK, Vice President, Global Delivery Head - Network Engineering Services, Infosys

(Disclaimer: The views expressed in this article are solely those of the author and do not reflect CyberMedia’s stance.)