According to a report by Mark Albertson in SiliconANGLE, edge computing platforms are undergoing a structural transformation to handle artificial intelligence workloads. In the past, edge computing sites were regarded as a secondary back-end support function and as smaller, more remote copies of a data center. Now, as agentic AI intensifies infrastructure demands, the edge is evolving into a dedicated AI-ready compute system with optimized hardware, centralized visibility, remote deployment, and full-stack lifecycle management. Industry players, including Cisco Systems Inc., are working to alter legacy edge infrastructure models, which were never built for advanced and data-intensive applications, by introducing platforms and management capabilities for distributed workloads.
James Leach, director of product management at Cisco, noted in an interview with theCUBE that there is a trend of workloads migrating from data centers toward the edge. According to Leach, the shift is not being carried out simply for the sake of going to the edge, but is driven by where data is generated. Leach explained that when processing data where it lives, it can be processed much more efficiently compared to trying to move it back to a centralized cloud or data center. He compared data to oil, noting that it must be refined and processed as close to its production site as possible.
Addressing Infrastructure Demands via Cisco Unified Edge
Cisco’s Unified Edge solution, which earned the company the 2026 Tech Innovation CUBEd Award for the most innovative IoT or edge platform, was designed to address the infrastructure demands created outside the data center. The system debuted in November 2025 as a converged hardware platform that combines compute, networking, and storage in a modular architecture for real-time AI inferencing at the edge. The platform supports central processing units (CPUs) and graphics processing units (GPUs), up to 120 terabytes of storage, redundant power and cooling systems, and integrated 25-gigabit networking.
The stated goal, according to Leach, is to provide a comprehensive core-to-edge framework for processing AI workloads, rather than a disconnected set of tools. Cisco integrated its Intersight management platform into the system, allowing organizations to centrally monitor and manage infrastructure distributed across thousands of edge locations. Leach noted that the fleet management capabilities and additional features within Intersight are designed to reduce operational complexity and enable customers to achieve value from their AI infrastructure at scale outside the data center.
Transition to an Intelligence-Centric Topology and New Traffic Patterns
Cisco’s work reflects a transition from an application-centric infrastructure model to an intelligence-centric topology. Analysts at theCUBE Research have documented a four-layer operating framework: Frontier Model, Cognitive Surface, Transactional Substrate, and Edge. According to the analysis, enterprises able to swiftly adopt this operating framework can lower total costs, speed decision cycles, and improve the potential payback on AI investments, while turning the network into a central factor in supporting these infrastructures.
Dave Vellante, chief analyst at theCUBE Research, explained that east-west data traffic expands as AI agents call tools and coordinate tasks, and that operational tooling is required to keep environments stable under heavy load. According to data cited by Cisco, agentic workflows may generate approximately 450% more network traffic than equivalent human-driven processes. In this context, Jeff Schultz, senior vice president of portfolio strategy at Cisco, noted that while humans click interfaces, agents operate in swarms.
In June, Cisco introduced Cloud Control, a unified management platform for human operators and AI agents running critical IT infrastructure. Because agents communicate with cloud-based models and services, significant upstream traffic is generated, increasing demands for low latency, resiliency, and security, changing the equation in which enterprise networks, historically characterized primarily by downstream traffic, must now adapt to accommodate heavier uploads as well. In this context, Schultz noted that upload must be considered, since when an agent needs to interpret a skill it accesses a large language model (LLM) that may sit in a cloud.
An Integrated Security Concept Across Network and Edge
The growth in network traffic has also led to an increased emphasis on security. In June 2025, Cisco expanded its Hybrid Mesh Firewall strategy, a distributed security architecture that extends policy enforcement across data centers, clouds, and distributed environments. The portfolio includes Cisco and third-party firewalls, Cisco Hypershield, and Cisco Secure Workload. Similarly, the Unified Edge platform incorporates multilayer zero-trust protections, telemetry, and policy controls integrated into the platform.
Developments in AI have influenced how enterprise security is designed. Anthropic’s unveiling of Claude Mythos Preview in April highlighted the advancing cybersecurity capabilities of frontier models. Anthropic launched Project Glasswing to apply these capabilities defensively, but its cybersecurity research also showed that advanced Claude models can construct active exploits under controlled conditions. In response to these threats, Cisco expanded Live Protect in June, a runtime security feature that deploys compensating controls in real time to block vulnerability exploitation without requiring reboots, upgrades, or downtime. Leach noted that in an almost post-Mythos type world, security cannot be bolted on after the fact but must be integrated across every layer of the system.
Consolidating Controls and Infrastructure for AI Agents
Cisco’s strategy is based on the assessment that the operational complexity of agentic AI will require infrastructure consolidation, with the network serving as a foundational baseline. Against pure chipmakers and software-only vendors, operations and security stacks serve as competitive differentiators.
Vellante of theCUBE Research noted that enterprises seek integrated outcomes rather than a portfolio of parts, and that 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. Bob Laliberte, principal analyst at theCUBE Research, added that customers are not required to purchase the entire portfolio, but the value proposition improves when networking, security, observability, collaboration, and customer experience components are deployed together, enabling staged adoption with operational integration and AI-driven workflows.