Unveiling AI's Potential: The Importance of Your Computer Network as the Access Point
In the rapidly evolving world of technology, the emergence of Agentic AI adds a new layer of complexity for IT teams. This development, consisting of autonomous agents that work together across various tools, APIs, and knowledge domains, risks the creation of "shadow AI" if network visibility and control are not unified.
Enterprises must stop viewing networks as mere conduits and start treating them as intelligent platforms. These platforms should be capable of contextual decision-making, enforcing zero-trust security, and enabling agile, cloud-native operations. The AI era makes the network a critical security enforcer, given AI's inherent reliance on vast datasets and its status as a prime target for cyber threats.
AI-driven environments generate large volumes of east-west and north-south traffic that conventional network architectures struggle to handle due to their rigidity, appliance-heaviness, and siloed nature. The evolution of networking must be rebuilt from the ground up: fully cloud-native, programmable, and ready to support AI at enterprise scale.
The network infrastructure of the future must be inherently simple, secure, and scalable, encompassing multi-cloud environments, enterprise connectivity, and a robust cloud backbone. Intelligently managed, software-defined networks can help bridge the gap by automating diagnostics, enforcing security policies, and dynamically responding to changes.
Agentic AI systems also utilize LLM orchestration across various models. In domains such as real-time healthcare analytics, autonomous manufacturing, or financial modeling, network disruptions can lead to operational or financial loss. A modern network layer is essential for Agentic AI, LLM environments, and real-time decision engines to operate securely and reliably.
Modernizing the network infrastructure is a strategic imperative for enterprises aiming to pre-empt risks and lead in the AI era. Organizations need a network foundation to transition from experimental AI to enterprise-class AI operations that are secure, compliant, and agile.
Key network requirements for supporting real-time AI applications and ensuring network security in AI-driven environments include high speed and high bandwidth, low latency, network agility and resilience, advanced security integration, scalability and manageability, support for distributed and edge AI, and the delivery of a true cloud-native experience.
These Agentic AI systems rely on memory systems to track context and history. The network must deliver a true cloud-native experience, providing granular control, comprehensive visibility, and extensive automation for AI and core business operations. Modern networks must have integrated security controls, elastic scalability, intelligent observability and automation, and AI-native design.
The pace of AI adoption demands the ability to define, visualize, and deploy resilient network architectures within minutes. The expanding complexity of modern IT ecosystems and the specialized nature of AI workloads are widening the existing talent gap, making it difficult for organizations to find and retain network and security expertise.
[1] Networking for AI: Key Requirements and Best Practices. (n.d.). Retrieved from https://www.cisco.com/c/en/us/solutions/collateral/service-provider/networking-for-ai/white-paper-c11-7393938.html
[2] Networking for AI: Enabling a High-Performance AI-Driven Network. (n.d.). Retrieved from https://www.hpe.com/us/en/insights/articles/networking-for-ai-enabling-a-high-performance-ai-driven-network.html
[3] Networking for AI: A Guide for Enterprise IT. (n.d.). Retrieved from https://www.ibm.com/cloud/garage/ai/networking-for-ai
[4] Networking for AI: Best Practices for Network Performance. (n.d.). Retrieved from https://www.vmware.com/content/dam/digitalmarketing/vmware/en/pdf/solutions/networking-for-ai-best-practices-for-network-performance.pdf
Data-and-cloud-computing technology plays a significant role in supporting Agentic AI systems, particularly in the areas of memory systems that track context and history. The network infrastructure, modernized with a cloud-native design, offers granular control, comprehensive visibility, and extensive automation for AI and core business operations.
In the move towards enterprise-class AI operations, organizations require a network foundation that meets key requirements such as high speed, low latency, network agility, advanced security integration, scalability, support for distributed and edge AI, and the delivery of a true cloud-native experience. These initiatives are essential for enabling secure and reliable Agentic AI, LLM environments, and real-time decision engines in various domains like healthcare analytics, autonomous manufacturing, and financial modeling.
