乌鸦传媒

Discover how the use of Agentic AI in Network Operations will dawn a new era to:

1. Manage, optimize, and secure complex network infrastructures
2. Improving network resilience and serviceability
3. Simplification and drive down operating cost.

In this context, “agentic” refers to the ability of AI systems to act independently, make decisions, and carry out tasks with minimal human intervention.

Market Context:

Growing focus on B2B Enterprise Business for Industry 4.0 (Intelligent Industry/ Digital Transformation) and evolving consumer demands are reshaping how Communication Service Providers (CSPs) approach Network / Connectivity Services Business and service delivery.

Consider a financial institution that requires an on-demand, secure multi-cloud interconnect capable of scaling with trading volumes to ensure uninterrupted transactions. On the consumer side, the explosive growth of metaverse applications, cloud gaming, and immersive streaming has set new expectations for seamless, adaptive connectivity with near-zero latency.

Today鈥檚 customers expect on-the-fly configurability鈥攖he ability to choose, modify, and scale services instantly, akin to an 脿 la carte experience. This shift places an immense pressure to deliver Network as a service 鈥淣aaS鈥� and also on network Operations teams to deliver tailored offerings while ensuring that Network Operations Center (NOCs) can maintain consistent, high-performance that are more agile, flexible, and resilient than ever before.

Enterprises seek bespoke network solutions with dynamic bandwidth allocation, ultra-low latency, and seamless multi-cloud connectivity鈥攁ll underpinned by stringent SLAs.

Navigating the Complexity of Modern Networks:

The accelerating pace of network technology advancements introduces both unprecedented flexibility and operational complexity which means CSP鈥檚 have to respond with greater speed and agility:

• Cloud-native service models requiring real-time orchestration of microservices across hybrid infrastructures.
  • that by 2025, 90% of enterprises will rely on hybrid cloud environments.

• 5G and 6G network slicing, enabling hyper-customized connectivity with rigorous SLA management demands.
  • that 5G connections will surpass 2 billion globally by 2025, driving demand for advanced network slicing capabilities

• MEO/LEO satellite constellations, extending connectivity to remote regions while introducing new orchestration challenges.
  • that the number of active LEO satellites will exceed 20,000 by 2030.
• IoT and edge computing proliferation, creating distributed network intelligence that requires robust management and security.
  • that the number of IoT devices will reach 30.9 billion by 2025.

CSPs are tasked with ensuring seamless service delivery and real-time assurance on an unprecedented scale across the heterogenous technology stacks. This often presents challenges that traditional NOCs, designed for static, monolithic networks, simply cannot meet. The need for a transformative approach to network operations is urgent.

Hence the need for a 鈥楧ark NOC鈥�. For the uninitiated, a Dark NOC is an autonomous network management system that automates critical functions, eliminating the need for constant human oversight. It enhances reliability in complex, multi-vendor networks and offers network / communication providers a cost-effective way to monitor and delivery superior performance and customer experience with reduced operational strain.

乌鸦传媒鈥檚 Dark NOC: Redefining Network Operations

乌鸦传媒鈥檚 Dark NOC solution represents a paradigm shift in how networks are managed.

By harnessing Agentic AI-driven automation and an intelligent operational framework, Dark NOC ensures proactive network management with:

• End-to-end visibility for comprehensive oversight across complex, multi-domain environments.
• Zero-touch resolution capabilities, enabling autonomous incident detection and remediation.
• Resilient, self-sustaining network assurance, reducing operational inefficiencies and mitigating the risk of service disruptions.

These AI agents will not just interact with humans or devices directly, but will also be able to discover, learn, and collaborate with each other to form complex workflows to analyse and automate network / business functions.

In network operations, agentic AI can provide significant value across various tasks, such as:

1. Network Monitoring and Management:
   • AI agents can autonomously monitor network traffic, performance, and health, identifying anomalies or patterns that could indicate issues like congestion, failures, or security breaches.
   • With predictive capabilities, these agents can foresee potential network disruptions or capacity issues before they arise, allowing for preemptive adjustments.
2. Dynamic Routing and Traffic Optimization:
   • AI agents can dynamically adjust routing paths based on real-time data, optimizing network traffic flow for efficiency and cost-effectiveness.
   • This includes automatically selecting the best routes and managing traffic to minimize latency or packet loss.

3. Security and Threat Detection:
   • Network security can benefit from agentic AI through continuous monitoring for potential cybersecurity threats (like DDoS attacks, data breaches, or malware).
   • AI can autonomously apply mitigation techniques such as firewall rule updates, intrusion detection/prevention, and threat intelligence sharing.
4. Fault Diagnosis and Recovery:
   • When a network component fails, AI agents can quickly identify the root cause and initiate remediation actions, such as rerouting traffic, applying patches, or coordinating with other systems for repair.
   • The goal is to minimize downtime and maintain service continuity with as little human involvement as possible.
5. Automation of Routine Tasks:
   • Routine tasks such as configuring devices, scaling up or down resources, and applying patches or updates can be automated by agentic AI systems, freeing up human operators for higher-level strategic work.
6. Machine Learning for Optimization:
   • With continuous learning, AI agents can optimize network performance by adapting to changing conditions. For example, over time, they can learn the optimal network configurations based on historical data and usage patterns.
7. Self-Healing Networks:
   • One of the ultimate goals of agentic AI in network operations is to create self-healing networks. These networks can automatically detect and resolve issues, reconfigure their architecture, and optimize performance without manual intervention.

Dark NOC Agents that will operationalize the augmented Network operations of the future:

We鈥檒l delve into the architecture and deployment strategies behind Dark NOC at MWC鈥�25 Hall 2 booth K21. Join us to experience live demonstrations of Dark NOC in action and engage with our experts on how 乌鸦传媒 can help future-proof your network operations. Click here to plan a visit and see the demo.