The modern edge computing platform has arrived.
Before the explosion of artificial intelligence, edge computing sites were generally regarded as a back-end support function, smaller and more remote copies of a data center. However, as agentic AI has intensified infrastructure demands, the edge has emerged as an AI-ready compute system in its own right, with optimized hardware, centralized visibility, remote deployment and full-stack lifecycle management.
The challenge is that older edge infrastructure models were never built for AI and advanced, data-intensive applications. Leading industry players such as Cisco Systems Inc. have sought to change that dynamic, introducing new platforms and management capabilities for distributed workloads.
Cisco’s approach is straightforward: As intelligence becomes more distributed, enterprises increasingly need to process data closer to where it is generated.
“There has been a trend going on for quite some time actually where we’re seeing some workloads from the data centers migrate towards the edge,” said James Leach, director of product management at Cisco, during an interview with theCUBE, SiliconANGLE Media’s livestreaming studio. “It’s not necessarily migrating to the edge for the sake of going there; it’s more migrating towards the data. A lot of that is based on the fact that where the data lives, we can process it much more efficiently than if we were to try to move it back to some centralized cloud or data center. If you think of data as the new oil, as they say, we have to go and process it and we have to refine it as close to the mining of it as possible.”
This feature is part of SiliconANGLE Media’s exploration of the architectural shifts powering continuous, production-grade AI. Be sure to check out theCUBE’s conversation with Cisco’s James Leach as part of a special award recognition broadcast.
Edge computing tackles new infrastructure demands
Leach spoke with theCUBE as part of an exclusive conversation about Unified Edge, which earned the company the 2026 Tech Innovation CUBEd Award for the most innovative IoT or edge platform. The solution is designed to address the growing infrastructure demands created by AI workloads outside the data center.
Cisco’s Unified Edge debuted in November 2025 as a converged hardware platform that combined computing, networking and storage in a modular system designed for real-time AI inferencing at the edge. It supports central processing units and graphics processing units, up to 120 terabytes of storage, redundant power and cooling and integrated 25-gigabit networking. The goal, according to Leach, is to provide customers with a full core-to-edge framework for processing AI workloads.
“It’s giving it to a customer and showing them, ‘Hey, here’s a solution’ instead of, ‘Here’s a set of tools to fix your problem,’” Leach said. “This is not an edge-type management solution, and we have the data center management solution side-by-side. This is one solution that can manage from the data center all the way to the edge.”
To deliver that core-to-edge capability, Cisco integrated its Intersight management platform, allowing organizations to centrally monitor and manage infrastructure distributed across thousands of edge locations. Intersight plays a key role in providing a simpler way to manage the many moving parts of AI infrastructure.
“With some of the fleet management capabilities and some of the other capabilities that we have within Intersight, we’re able to take that complexity away and actually make it simpler, make it much more easier for the customer to achieve that ‘zero-to-actually-getting-value’ out of their AI infrastructure at scale,” Leach said. “Now we’re taking it out of the data center.”
Intelligence-centric framework
Cisco’s work with Unified Edge and Intersight illustrates how AI is driving enterprise IT to shift from an application-centric model to an intelligence-centric topology. As theCUBE Research analysts have documented, this is creating a four-layer operating framework — Frontier Model, Cognitive Surface, Transactional Substrate and Edge — that reshapes cost, latency, governance and infrastructure design.
The implications of the new framework are significant. Enterprises able to swiftly adopt this operating framework can lower total cost and speed decision cycles, improving the potential payback on AI investment. This makes the network a first-class citizen in supporting growing agentic AI infrastructure and helping it run reliably.
“It’s network throughput, east-west traffic patterns that explode as agents call tools and coordinate work, and the operational tooling that keeps these environments stable under heavy load,” explained theCUBE Research Chief Analyst Dave Vellante. “This is where Cisco has an advantage. If agents increase workflow traffic and tool calls, the network becomes a multiplier, not just background plumbing.”
Cisco has cited data showing that agentic workflows can generate approximately 450% more network traffic than equivalent human-driven processes. As Jeff Schultz, senior vice president of portfolio strategy at Cisco, told theCUBE earlier this year, “Humans click, agents swarm.”
This means that network architecture must evolve to support new traffic patterns. Cisco’s introduction of its Cloud Control offering in June created a unified management platform for human operators and AI agents running critical IT infrastructure. Agents must communicate with cloud-based models and services, generating substantial upstream traffic while increasing demands on latency, resiliency and security. That changes an equation in which enterprise networks, historically dominated by downstream traffic, increasingly must accommodate heavier uploads as well.
“We have to be thinking about the upload, because as an agent sitting at a desk needs to interpret a skill, it’s going to an LLM that may sit in a cloud,” Schultz told theCUBE.
Rethinking systems security
Greater network throughput has also resulted in a heightened focus on security. A noteworthy element of Cisco’s approach is the company’s emphasis on integrating security directly into the network fabric.
In June 2025, Cisco expanded its Hybrid Mesh Firewall strategy, a distributed security architecture designed to extend firewall enforcement and consistent policy across data centers, clouds and distributed environments. The portfolio includes Cisco and third-party firewalls, Cisco Hypershield, Cisco Secure Workload and other enforcement points, illustrating Cisco’s broader effort to fuse security more deeply into network infrastructure.
Unified Edge follows a similar philosophy by building security into the edge system itself, with multilayer zero-trust protections, telemetry and policy controls integrated into the platform.
“We build this state of the system, we abstract the entire statefulness of a system into a profile,” Cisco’s Leach explained. “We’ve always done this. This is what makes UCS really unique. If you take that to the edge, now we’re saying, ‘Hey, let’s build this entire system not just around compute, but around observability, around security, around all of the pieces we want.”
AI has required security practitioners to rethink how enterprise networks and systems can be protected. Anthropic PBC’s unveiling of Claude Mythos Preview in April underscored how quickly the cybersecurity capabilities of frontier AI models are advancing. Anthropic described Mythos Preview as particularly capable at computer security tasks and launched Project Glasswing to apply those capabilities defensively while preparing for increasingly capable AI-driven attacks.
The concern extends beyond vulnerability discovery. Anthropic’s cybersecurity research has demonstrated that advanced Claude models can also construct working exploits under controlled conditions, compressing work that once demanded significant human expertise and time. This has led Cisco, along with other major enterprise players, to reevaluate how security is designed and implemented. In June, Cisco expanded the reach of Live Protect, a runtime security feature for network infrastructure that deploys real-time compensating controls to block malicious exploits without requiring reboots, upgrades or downtime.
“We’re living in an almost post-Mythos type world right now where security has to be thought of differently,” Leach noted. “I’m securing every single element, and that’s the kind of security that can’t be bolted on after the fact. It has to be baked in at every single level.”
Unified control plane for agents
Cisco’s strategy is grounded in a belief that the operational complexity of agentic AI will require consolidation, with the network providing a solid foundation. When measured against chipmakers and software-only companies in the AI arena, the operations and security stacks become competitive differentiators.
Unified Edge’s modular architecture provides options for enterprises in the still-evolving world of AI. The offering’s operational management model demonstrates how Cisco is applying familiar data center approaches to edge deployments.
This illustrates how the technology industry is transitioning to a new operating model running at machine speed. It requires new infrastructure thinking and unified controls, as theCUBE Research’s Vellante points out.
“The risk is cohesion and speed,” Vellante said. “The enterprise wants integrated outcomes – not a portfolio of parts. Cisco is showing signs that it can deliver a unified control plane for agentic systems across networking, security, observability, and operations, combined in a way that reduces friction for customers.”
The key for Cisco will be how successfully it can transform from a collection of networking, security, collaboration, customer experience and observability businesses into a fully integrated platform company. Cisco believes this is what the market wants as AI infrastructure becomes more distributed and interconnected.
“Customers are not required to buy the entire portfolio,” noted Bob Laliberte, principal analyst at theCUBE Research. “That said, the value proposition improves significantly when networking, security, observability, collaboration, customer experience, and AI services are deployed together. This approach allows customers to adopt technologies incrementally while still benefiting from cross-domain visibility, shared telemetry, unified operations, and AI-driven workflows. In many ways, its integrated portfolio may represent Cisco’s most important competitive differentiator moving forward.”
