By Alan J Weissberger

Executive Summary:

The rapid rise of agentic artificial intelligence (AI) is expected to drive material changes across data centers, service provider networks, and the broader telecom ecosystem. As agentic AI moves from chat-oriented interactions to autonomous digital agents, Cisco says that those workloads will not only increase traffic volumes, but also alter traffic characteristics in ways that place new demands on latency, security, orchestration, and distributed compute placement.

“We are entering into a Network Supercycle,” Jeetu Patel, Cisco’s president and chief product officer, said during his opening keynote at Cisco Live in Las Vegas.

As a result, network operators will need more resilient transport, edge compute, and optical capacity to support new traffic patterns and security demands.

Cisco execs pictured (left to right): Jeetu Patel, president and chief product officer; Chuck Robbins, chairman and CEO; Liz Centoni, EVP and chief customer experience officer; and Steven Clayton, SVP and chief communications officer.

Source: Jeff Baumgartner/Light Reading

AI Traffic Impact on Transport Requirements:

From a transport perspective, agentic AI traffic is likely to be more persistent, more interactive, and more latency-sensitive than conventional application traffic. Cisco has said AI-related network traffic is expected to triple over the next three years, with inference flows emerging as a major driver of load growth. That shift could place pressure on transport architectures that were optimized primarily for human-driven web, video, and enterprise application traffic

The implication for service providers is that traffic engineering will need to evolve toward finer-grained path control, stronger telemetry, and improved handling of asymmetric flows. AI sessions that span multiple exchanges between users, applications, and digital agents may also require more sophisticated policy enforcement and security integration across WAN, metro, and access layers.

Edge Compute Needs Grow:

Cisco’s remarks also point to a growing role for edge compute in telecom and cable networks. Some operators are already repurposing legacy central offices and mini data centers to support AI workloads, reflecting a broader shift toward distributed inference close to the user or device.

That architecture matters because many agentic AI use cases will be latency constrained and will not perform efficiently if all processing is centralized in distant cloud regions. Comcast and Charter have both announced AI edge strategies, underscoring how access networks can become part of the compute fabric rather than acting solely as last-mile connectivity.

For network operators, this suggests a new operational model in which compute, storage, and network functions are increasingly coordinated across regional and edge sites. In practical terms, the network becomes part of the application execution environment, not just the transport layer beneath it.

Optical Network Implications:

Optical infrastructure will likely carry much of the burden created by distributed AI deployments. As inference workloads expand across regional hubs, edge sites, and centralized clouds, operators may need higher-capacity optical transport to sustain east-west traffic between distributed compute nodes.

That points to greater demand for dense 400G and 800G interconnects, more flexible wavelength management, and lower-latency optical paths between metro aggregation points and AI facilities. The challenge is not only to scale throughput, but also to preserve path diversity, minimize jitter, and maintain predictable performance for machine-to-machine workloads that are increasingly sensitive to delay.

As AI traffic becomes more dynamic and more operationally critical, optical networks may need to be engineered with the same level of service awareness traditionally associated with enterprise transport and carrier-grade voice or mobile backhaul.

Security is a Top Priority:

Cisco cited security as a serious concern for agentic AI traffic. CEO Chuck Robbins said AI agents designed to help enterprise customers can run roughshod without a proper defense that can quickly detect, intercept and possibly “kill” them before they get out of control. It becomes an even bigger issue when they are built to be nefarious.

“AI changes the speed of defense,” Robbins said. “It’s empowering adversaries at a pace that we haven’t seen in our careers … These [AI] models are as bad as they are ever going to be …They’re only going to get better.”

Anthropic’s new Claude Mythos model, which can auto-detect and possibly exploit software vulnerabilities at scale, is now a “CEO-level discussion,” he added.

“We’re living in a post-Mythos world where security has to be fused and baked into the network,” Patel said, holding that vulnerabilities can now being attacked as soon as they arise.

“We need to reimagine security” in the AI era, Patel said, noting that AI agents will not only handle tasks locally but will be heading outside to connect to third-party agents, servers and various tools.

“Every agentic action is a routing challenge, a trust decision and a telemetry event,” Patel said. The emergence of agentic AI, he said, is shifting the security and permission focus from “access control” (for us humans) to “action control” for agents that will need to be closely monitored, controlled and, if needed, quickly intercepted.

“People don’t trust these agents right now,” Patel said later during a separate discussion with press and analysts.

These concerns also extend to AI agent identity, which Cisco is addressing with its recent agreement to acquire Astrix Security.

This extends to other types of guardrails and observability metrics, too, including the notion of “tokenomics” – essentially keeping tabs on how many tokens an AI agent could consume. If the agent is found to be overspending on tokens, it could be intercepted and shut down.

Patel suggested that, without guardrails, what a company pays for AI tokens for a year could be consumed by an agent in a week. Assessing such AI agent behavior was a key driver of Cisco’s acquisition of Galileo Technologies.

Cisco’s AI Stack:

Cisco is focused on a vertically integrated platform – starting with its Silicon One platform for data centers and enterprise devices, optics, switches, routers and access points, apps and services, and wrapped by a new Cisco Cloud Control platform announced this week. Though Cisco Cloud Control is able to provide unified access to Cisco’s tools, apps and services, such as Meraki, Catalyst and Splunk, Patel stressed that it will also be able to integrate with third parties and support an open ecosystem. Cisco is starting out with support from 52 partners, including AWS, Google Cloud, NetBrain and ServiceNow.

Telecom Market Transition:

Robbins said Cisco used AI to scan 1.8 billion lines of code in 25 different programming languages over the past eight weeks. Without AI models, that would’ve taken eight years, he said.

Patel described the industry as being at a pivotal moment, moving from chat bots to more advanced agents that function as “digital coworkers.” He noted that “These agents are going to be everywhere.”

That transition suggests telecom networks will increasingly support autonomous machine interactions at scale, with implications that extend beyond bandwidth growth into security, policy control, and distributed systems design. For operators and vendors alike, the strategic question is no longer whether AI will affect the network, but how quickly the network architecture can adapt.

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References:

https://www.lightreading.com/ai-machine-learning/cisco-ai-driving-a-network-supercycle-

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