Network evolution for the Agentic AI era
With all of the attention being paid to the compute resources required to power AI, connectivity is sometimes overlooked. This poses a new dynamic for those planning their next phase of AI deployment. Those who modernize their IP networks can unlock new revenue from AI-driven services, while those who delay risk losing competitive relevance.
To benefit from the amazing capabilities AI brings, organizations need networks that can adapt dynamically as AI agents request data, trigger actions, and collaborate across distributed, multi-cloud environments. Concepts like “busy hour” traffic models of the past are giving way to always-on traffic profiles with continuous demand. AI agents are hitting the network around the clock, making decisions in microseconds. But traditional networks were built to deliver voice, video, and general internet traffic—not to provide the agility and performance demanded by AI workloads.
For one, the performance and speed of AI demand real-time telemetry to help operators better understand traffic patterns and support automated intervention. Without this real-time information, operators are left trying to support AI workloads through reactive manual troubleshooting, relying on static reports that, in most cases, are outdated by the time they are used.
Additionally, evolving from a bloated, rigid, and complex IP architecture to more modern ones based on segment routing and EVPN is necessary to provide a foundation for convergence and precise path control, enabling dynamic traffic routing as AI Agents’ connectivity needs change. In the past, network architects often had weeks to make changes to support new demands. Today, network conditions must change within seconds to meet the requirements of AI agents. While legacy IP networks and traditional protocols have served enterprises well throughout earlier eras of VPN and internet connectivity, they are too rigid and too complex for dynamic AI demands. Segment routing leverages existing network investments while creating the evolutionary path to the flexibility needed for AI workloads.
Finally, networks need FlexAlgo capabilities. Short for“flexible algorithm,” this feature lets the network calculate optimal paths for different traffic types. For example, one class of traffic might be optimized for latency, another for available bandwidth, another for resiliency, and another to satisfy data sovereignty requirements, depending on the needs of specific workloads. In many ways, FlexAlgo delivers the traffic-engineering benefits that operators once sought with RSVP-TE, but without the massive complexity. While RSVP-TE relied on manually engineered tunnels and extensive state management, FlexAlgo allows operators to define performance objectives and constraints, then lets the network automatically compute and maintain the appropriate paths. As networks increasingly support different SLAs for different AI agents and workloads, FlexAlgo ensures traffic is matched to performance requirements rather than constrained by static, one-size-fits-all rules.
Recently, Ciena has been working with a group of large enterprises in critical sectors such as healthcare and finance to incorporate all three capabilities, along with MACsec security, into their network architectures as part of broader digital transformation initiatives. These organizations needed to support a mix of AI and traditional workloads while ensuring that traffic adhered to strict policy, sovereignty, and SLA requirements. Depending on their operational model, they could deploy and manage their own IP networks over leased optical services from providers or consume the same capabilities through a fully managed network service, creating new opportunities for providers to deliver differentiated, value-added services.
The result is a network that automatically enforces business policies and performance objectives, preventing connectivity bottlenecks and maintaining service assurance as AI adoption and digital transformation efforts continue to scale.
AI creates both an opportunity and a challenge for service providers and large enterprises. If they modernize their IP networks, they can monetize the next wave of AI services. But if they stand still, they risk being run over by competitors who embrace network evolution.
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