The Huawei Network Summit 2026 South Africa concluded successfully in Johannesburg, drawing more than 400 industry leaders, technical experts, and ecosystem partners.  At the event, Huawei introduced its upgraded Xinghe Intelligent Network Solution for Southern Africa, now positioned under the “Secure and Intelligent Connectivity” framework.

The announcement underscores Huawei’s continued push to enable intelligent transformation across industries in collaboration with customers and partners.  As AI agents move from experimental deployments to mission-critical production environments, network requirements are shifting accordingly. Industry attention is increasingly moving beyond token consumption metrics to Daily Active Agents (DAA), reflecting the emergence of large-scale agentic AI adoption and the need for next-generation networks with stronger performance, resilience, and security.

Leon Wang, President of Huawei’s Data Communication Product Line, said: “Real-time AI interaction, multidimensional data flow, core business security, and other scenarios are driving networks to embrace an AI-centric paradigm shift. This marks a transition from ubiquitous ultra-broadband into a new journey defined by lossless computing power, integrated communication and sensing, full-scope security, and network autonomy.”

Powered by a next-generation intelligent network foundation, Southern Africa’s digital and intelligent transformation is entering a new phase, according to Huawei.

“Johannesburg is a vibrant city rich in opportunities, connecting talent, industries and cultures across Africa,” said Vincent Chen, Vice President of Enterprise Business, Southern Africa Region, Huawei. “Today, AI is becoming a key driver of global innovation, and its adoption in Africa is transitioning from pilot exploration to real-world deployment, accelerating intelligent transformation across sectors such as finance, education and public services. For the Southern African market, Huawei’s goal is to advance intelligence across industries by collaborating with industry partners to build intelligent, secure and reliable network infrastructure for the AI era.”

The rapid growth of AI agents is creating new use cases and requirements, placing unprecedented technical demands on network infrastructure.

“Today’s enterprise network infrastructure faces four major challenges on its path to digital and intelligent transformation. These include the ever-widening gap between computing supply and demand; traffic pattern shifts driven by AI agents; surging O&M complexity; and new AI-driven attacks compounding the vulnerabilities of new systems,” said Arthur Wang, Vice President of Huawei’s Data Communication Product Line. “To address these challenges, Huawei has upgraded its Xinghe Intelligent Network Solution under a new paradigm of ‘Secure and Intelligent Connectivity.’ The first is an intelligence upgrade, expanding AI beyond O&M into the entire network. The second is a security upgrade, advancing from single-point defence to end-to-end protection that deeply converges network and security. Through these two key upgrades, we aspire to build a solid connectivity foundation for every enterprise in the Agentic AI era.”

During the event, Huawei also unveiled its upgraded Xinghe Intelligent Network product portfolio and the Xinghe AI Cloud Campus SaaS Service Platform for Southern Africa.

Shi Lei, Vice President of the NCE Data Communication Domain of Huawei’s Data Communication Product Line, said:

“In the past, intelligent O&M was a luxury exclusive to large enterprises. Now, we have deeply integrated AI into the cloud management service platform, enabling SMEs to easily access these capabilities as a cloud service. This is more than tech inclusion; it is about making AI network services genuinely accessible, affordable, and actionable.”

Analysis & Opinion:

Huawei’s upgraded Xinghe Intelligent Network Solution for South Africa reflects a clear shift toward AI-native enterprise networking, with Huawei positioning the platform around “secure intelligent connectivity.” In practical terms, the upgrade extends AI beyond operations and management into the broader network fabric, while also tightening the convergence of networking and security across campus, WAN, data center, and security domains. The announcement also ties the solution to the broader “Agentic AI era,” which suggests Huawei is targeting workloads where connectivity, automation, and security need to operate together.

For South African enterprises, the strategic value is clear: AI adoption is pushing networks to support heavier east-west traffic, lower latency, stronger segmentation, and more autonomous operations. Huawei is effectively arguing that traditional, siloed infrastructure is no longer sufficient for production AI environments.  Instead, the network must become a more autonomous, security-aware control layer that can sustain business continuity and scale with intelligent services.

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Disclaimer:  Perplexity.ai was used for research used to generate this article.

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Introduction:

Today, U.S. Senator Deb Fischer (R-Neb), Chairman of the Senate Commerce Subcommittee on Telecommunications and Media, convened a hearing examining how artificial intelligence (AI) is transforming telecommunications networks and how the technology can enhance services across America.  Titled “Intelligent Networks: Powering Artificial Intelligence and Transforming Communications,” the hearing examined the bidirectional relationship between AI and network infrastructure. In particular, AI demands low-latency, high-bandwidth networks, while also offering tools to make those networks more efficient and secure.

Witnesses:

• Jonathan Spalter, President and CEO, USTelecom — The Broadband Association

• Dan Watermeier, Commissioner, Nebraska Public Service Commission

• Bob Everson, Chief Architect of Provider Mobility, Cisco

• Asad Ramzanali, Director of AI and Technology, Vanderbilt Policy Accelerator

From Senator Fischer’s opening remarks:

“As AI adoption increases, so will the demand for reliable and resilient communications infrastructure. Networks are the backbone along which the enormous amounts of data associated with AI are transmitted. I look forward to discussing both how networks are adapting to respond to AI and how AI is being used in networks to proactively plan for the future.”

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Permitting Reform Dominates Discussion

The clearest consensus across industry witnesses was that outdated permitting processes are the primary barrier to deploying AI-ready fiber infrastructure. Spalter testified that “the biggest barrier to building the broadband infrastructure our country needs isn’t technology or investment — it’s outdated permitting processes,” and urged Congress to establish consistent permitting timelines while preserving environmental and historic review requirements.

Watermeier emphasized that fiber is the only broadly deployable technology capable of supporting AI-era traffic, noting that “fiber optic networks can greatly exceed” the FCC’s current 100/20 Mbps threshold. Everson echoed the urgency, stating that providers are ready to build if permitting can be accelerated. Sen. Shelley Moore Capito (R-WV) supported establishing permitting “shot clocks.”

Spalter also identified cybersecurity and sustainable broadband funding as essential priorities, though specific proposals on either topic were not detailed in reported testimony.

Digital Divide and BEAD Funding

Sen. Lisa Blunt Rochester (D-DE) pressed witnesses on the impact of the Trump administration’s approximately 74% cut to the Broadband Equity, Access, and Deployment (BEAD) Program. Ramzanali responded that unconnected households are excluded from AI’s economic benefits, telehealth access, and educational tools, stating: “We shouldn’t accept the state of the country where not every American is connected to high-quality networks”

Grid Reliability and Spectrum: Largely Absent

Despite the hearing’s framing, two critical topics received little direct attention. Grid reliability — a pressing concern given that PJM Interconnection reported data-center-driven supply cost increases exceeding 60%, and Bank of America projected ~125 GW of new U.S. electric load from data centers by 2030 (Legis1) — was not substantively addressed by witnesses.

Spectrum policy was similarly underexplored, despite significant adjacent developments: the FCC’s July 22 vote to auction 160 MHz of upper C-band spectrum, NTIA’s $53 million funding announcement for secure AI-enabled Radio Access Networks, and a detailed spectrum reform brief published by the International Center for Law & Economics timed to the hearing. The ICLE brief recommended five reforms: preserving a balanced mix of licensed, unlicensed, and shared spectrum; streamlining the Spectrum Relocation Fund; strengthening FCC-NTIA coordination; replacing worst-case interference analysis with risk-informed probabilistic methods; and coherent U.S. engagement at the ITU World Radiocommunication Conference to counter Chinese influence in standards bodies.

Cybersecurity: Listed but Undefined

Spalter listed cybersecurity among his three essential priorities but did not elaborate on specific threats or mitigation strategies. The absence is notable given that an adjacent House hearing on July 22 featured testimony from Lindsay Gorman warning that AI is “expanding the cyberattack surface” through prompt-injection attacks, data poisoning, and model exploitation. Gorman urged Congress to mandate AI cybersecurity standards and recommended next-generation networks implement AI-automated defenses and post-quantum cryptography.

Industry vs. Regulatory Divergence

The hearing revealed a clear fault line. The three industry witnesses uniformly advocated for reducing regulatory friction and enabling private investment. Ramzanali, whose broader research at Vanderbilt advocates utility-style regulation of digital infrastructure, structural separation of AI hardware and software, and a dedicated digital regulator, provided the only counterweight — though his reported testimony focused on digital divides rather than his full structural reform agenda.

Sen. Fischer’s opening remarks captured the industry-aligned framing: “Opaque regulations and lack of coordination should not get in the way of network development.” Sen. Blunt Rochester’s questioning represented the regulatory perspective, challenging funding cuts and emphasizing equitable access.

Outlook

The hearing underscored that the U.S. telecommunications policy debate around AI is currently dominated by infrastructure deployment concerns — particularly permitting — while cybersecurity, spectrum management, and grid reliability remain underexamined. With the Senate Commerce Committee delaying broader AI legislation markup until after the summer recess (Washington Times), the substantive policy work on these gaps may not advance until fall.

Full written testimony and a hearing transcript are not yet available on the committee website.

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Analysis via Perplexity.ai:

Comparison Matrix: Key Recommendations by Topic

1. Grid Reliability and Energy Demand

2. Cybersecurity Vulnerabilities

3. Spectrum Policy

4. Regulatory Reform vs. Government Oversight

5. AI-Enabled Services: Industry Priorities vs. Regulatory Proposals

Key Divergences and Synthesis

Consensus Points

• Permitting reform is urgent. All three industry witnesses and Republican senators agreed that permitting delays are the single biggest barrier to AI-ready network deployment. Even Sen. Fischer’s opening remarks flagged “opaque regulations.”

• Fiber is foundational. Spalter, Watermeier, and Everson all positioned fiber as the backbone infrastructure for AI. Fischer encapsulated it: “AI runs on infrastructure, and infrastructure runs on fiber.”

• AI transforms networks bidirectionally. Fischer’s framing — that AI requires better networks but can also make networks more efficient and secure — was implicitly accepted across witness testimony.

Sharpest Divergences

• Industry self-regulation vs. structural oversight. The three industry witnesses (Spalter, Watermeier, Everson) uniformly advocated for removing regulatory friction and letting private capital deploy infrastructure. Ramzanali’s framework — developed at Vanderbilt and reflected in his testimony on digital divides — argues for utility-style regulation, structural separation, and a dedicated digital regulator. This is the fundamental fault line.

• BEAD funding. Industry witnesses mentioned “sustainable broadband funding” as a priority but did not challenge the Trump administration’s 74% cut to BEAD. Sen. Blunt Rochester and Ramzanali directly attacked the cuts as harmful to AI equity. Industry silence on BEAD cuts suggests a pragmatic accommodation with the administration’s budget priorities.

• Cybersecurity specificity. Spalter listed cybersecurity as essential but offered no concrete proposals in reported testimony. This stands in contrast to the adjacent House hearing where witnesses like Lindsay Gorman called for mandated congressional cybersecurity standards. The gap between listing cybersecurity as a priority and proposing actual security mandates represents a significant industry-regulatory divergence.

• Spectrum was largely absent. Despite ICLE publishing a detailed five-reform spectrum brief timed to the hearing, and despite the FCC’s July 22 C-band auction vote and NTIA’s $53M AI-RAN funding announcement, none of the witnesses’ reported testimony engaged substantively with spectrum policy. This is a notable omission given that Everson’s title (Chief Architect of Provider Mobility) implies wireless expertise. The ICLE brief’s warning that “the United States cannot lead at digital speed while governing the airwaves at bureaucratic speed” went unaddressed in the hearing room.

Implications for Next-Generation AI-Enabled Services

• The hearing’s overwhelming focus on permitting reform and fiber deployment — while important — left cybersecurity, spectrum, and grid reliability largely underexplored. The most consequential gap is the absence of detailed cybersecurity testimony, given that AI is simultaneously expanding the attack surface and offering new defensive tools.

• Ramzanali’s presence provided the only counterweight to the industry consensus, but his reported testimony focused narrowly on digital divides rather than his broader structural reform agenda. Whether his Vanderbilt research on AI infrastructure financial risks and digital utility regulation will influence future Senate action remains to be seen.

• The ICLE spectrum framework — published as context for this hearing — represents the most detailed policy roadmap for AI-enabled wireless services, but it was not directly debated by the witnesses. The FCC’s C-band auction and NTIA’s AI-RAN funding are proceeding on parallel tracks outside the hearing’s scope.

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References:

Fischer Leads Hearing on AI in Communications Networks

NEWS: Senator Blunt Rochester Highlights How AI Will Impact Digital Divides

Introduction:

IEEE Techblog readers are keenly aware of the stupendous AI capex that has eliminated most hyperscaler free cash flow.  There’s also the ROI question when there’s no “killer app” or a clear way to monetize AI services.  And let’s not forget issues like: the competition for AI benchmark bragging rights. price per token, rack density, and power consumption-per-dollar.

Now the next AI battleground will be competition from Chinese open-weight models, which are pushing AI toward commoditization faster than many U.S. hyperscalers expected.  That shift could quietly erode the economics of the entire AI infrastructure stack.

Raffi Krikorian, the chief technology officer at Mozilla, which runs the Firefox browser, switched to Chinese AI startup Moonshot’s Kimi K3 for many of his day-to-day activities within days of the new, powerful model’s launch more than a week ago.  “It just seems snappier,” Krikorian said of K3, comparing it with the acclaimed, higher-priced Claude Fable chatbot from Anthropic, the San Francisco private AI company with a $1 trillion assessed market value. Earlier, he had been using another strong Chinese model, Z.ai’s GLM-5.2, for everyday tasks such as managing his calendar, documents, and email.

Krikorian is among a growing number of Americans turning to Chinese AI systems, which are gaining traction worldwide because they are more affordable and increasingly efficient. U.S. companies such as cryptocurrency exchange Coinbase have said they are switching to Chinese AI models to help reduce costs. Their growing popularity has frustrated some U.S. tech giants, but barring an outright ban, these models are likely to keep attracting independent software developers in the U.S. and beyond.

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-

Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035

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by Shashi Kiran with Alan J Weissberger, ScD

Abstract:

This article presents the primary author’s point of view on networking technology and market evolution, as experienced it directly with his customers at Nile, where he serves as Chief Marketing Officer (CMO). A key theme is overlaying the impact of AI and its implications for network and network security architecture on a new network stack. We focus specifically on the diverse complexity and heterogeneity of the LAN, while drawing inferences to other areas in the broader enterprise network.

The article draws no information from other publications or references, except for the security breach data points derived from IDC, Gartner, and market surveys.  Hence, the References listed at the end of the piece are from related IEEE Techblog posts and Nile press releases chosen by this website’s content manager.

Definitions:

The enterprise network stack is much more than a protocol stack. It is the layered architecture of physical infrastructure, forwarding devices, control protocols, management systems, and security enforcement functions that interconnect users, endpoints, workloads, and cloud services across campus, branch, WAN, data center, and cloud domains. It typically includes access, distribution, core, and edge segments, along with overlay, orchestration, telemetry, identity, and policy planes that govern how traffic is admitted, routed, segmented, monitored, and secured.

A useful way to think about the stack is in terms of planes:

• Data plane: forwards packets, enforces QoS, and applies access-control functions close to the traffic path.

• Control plane: discovers topology and capabilities, computes paths, and reacts to failures.

• Management plane: handles configuration, monitoring, troubleshooting, reporting, and performance management.

• Security stack: includes firewalls, IDS/IPS, secure web gateways, threat intelligence, and related inspection or enforcement tools.

At the device level, the stack typically includes physical media and network hardware such as cabling, Wi-Fi, NICs, switches, routers, gateways, servers, and dedicated security appliances. At higher layers, it includes protocols and services for addressing, routing, transport, application connectivity, identity, and policy enforcement, often mapped loosely to OSI/TCP-IP concepts rather than a strict textbook stack.

In an enterprise environment, the network stack extends across LAN, WAN, data center, cloud, and security domains, so “the stack” is less a single product and more an integrated system of infrastructure, software, telemetry, and policy. That is why discussions of enterprise architecture usually separate forwarding, orchestration, assurance, and security functions even when they are delivered in a unified platform.

Structural Limits of the Enterprise Network Stack:

The enterprise network stack is approaching a structural inflection which may be at a “breaking point.”  That’s because what’s failing is structural and architectural, not incremental.  The enterprise network stack was architected for a world that no longer exists, and most of the pain organizations feel today is the cost of pretending otherwise. The interesting question isn’t whether it breaks but rather when, and along which seams.  Here’s why:

The network stack most enterprises still run was designed around five assumptions that were partly true in 2010 but mostly false in 2026. Users sit at desks on managed devices. Applications live in a corporate data center. Traffic flows north-south through a perimeter. Identity equals a user with a session. Trust derives from network location. Every one of those is gone. Users are hybrid, apps are SaaS and multi-cloud, traffic is increasingly east-west and machine-driven, identity now includes non-human agents acting with delegated authority, and zero trust has formally retired the idea that being inside the network means anything.

So, the enterprise stack isn’t failing because any single piece is bad. Rather, it’s failing because the architecture it was based on no longer matches the workload, the threat model, or the operational reality it’s asked to serve. AI is the forcing function, but the cracks were already there. The choice in front of most enterprises isn’t whether to rebuild but whether to do it deliberately or by accident. Will reinvention and self-disruption be intentional or forced?

Today, many enterprise environments represent layered extensions of legacy architectures rather than cohesive designs. AI acts as an accelerant, exposing pre-existing architectural limitations. The resulting fragmentation increases operational complexity, reduces agility, and amplifies security risk.

Complexity is a Primary Risk Vector:

Complexity has evolved from an operational burden into a primary source of systemic risk. Modern network environments often exceed the capacity for deterministic human understanding, creating conditions where failures and vulnerabilities emerge at the intersections between systems rather than within individual components.

Empirical evidence suggests that many successful breaches exploit misconfigurations and integration gaps rather than novel vulnerabilities. In this context, complexity itself becomes the effective attack surface.

This challenge is particularly acute in the LAN, which often retains legacy architectural elements, heterogeneous device ecosystems, and fragmented management models. Combined with constrained IT resources, this environment can become a disproportionate source of exposure.

Reducing complexity—through architectural simplification, integrated control planes, and automation—is therefore not merely an operational objective but a core security strategy. In AI-driven environments, simplicity directly contributes to resilience and risk reduction.

An Architectural Reset is Needed:

An architectural reset is increasingly necessary. While incremental upgrades remain feasible, their marginal returns are diminishing relative to the growing mismatch between legacy designs and emerging requirements. Many organizations continue to extend existing architectures due to cost constraints or perceived transition risks. However, this approach often compounds technical debt and increases long-term exposure. The more fundamental question is not whether incremental evolution is possible, but whether it represents effective capital allocation in the context of AI-driven workloads and threat models.

Forward-looking architectures are converging around several principles: AI-native workload support, identity-centric security, zero-trust enforcement, and tightly integrated operational models. Organizations that proactively redefine their network architectures around these principles are more likely to achieve sustainable performance, security, and operational efficiency gains.

Here are a couple of conceptual architectural constructs for a unified, secure fabric with AI orchestration, autonomous operation and service delivery, which replaces the fragmented network stack and operations of the traditional/legacy network.   The first illustration is more  functional; the second is a more theoretical stack.  CLICK ON EACH IMAGE TO ENLARGE!

Security and the Network Fabric:

Security is neither fully “moving into” nor “remaining outside of” the network fabric; rather, it is being restructured across distinct functional planes, including identity, policy, enforcement, and detection.

Historically, network-centric security relied on in-path inspection mechanisms (e.g., firewalls, intrusion prevention systems, and proxies). This model proved difficult to scale due to encryption, cloud decentralization, and traffic patterns that bypass centralized inspection points.

In contemporary architectures, the network fabric is evolving into a high-performance enforcement plane. Policy definition and decision-making are increasingly centralized in identity and control-plane systems, while enforcement is distributed across the network and applied at line rate to identity-associated flows.

This separation of concerns improves scalability and composability. Identity-centric policy models define “who can do what,” while the network enforces those decisions efficiently and locally. The result is a more adaptable and performant security architecture.

However, the effectiveness of this approach depends on architectural discipline. Designs that treat the fabric as one component within a broader, identity-driven security framework tend to reduce complexity. Conversely, attempts to re-centralize security entirely within the network risk recreating earlier limitations in a more complex form.

AI’s Impact on Telecommunications Networks:

Artificial intelligence (AI) is influencing telecom network architectures along two orthogonal dimensions:

1.] AI introduces a new class of workloads that impose stringent and atypical requirements on network infrastructure.

AI workloads fundamentally challenge legacy network design assumptions. Traditional enterprise networks were optimized for north–south traffic patterns, human-driven interactions, and best-effort delivery models. In contrast, AI workloads generate predominantly east–west traffic, operate at machine timescales, and exhibit low tolerance for latency, jitter, and packet loss. Simultaneously, AI-enabled control and management planes enable higher degrees of automation and operational efficiency, particularly in campus and branch environments where autonomous operations are beginning to reduce manual intervention.

2.] AI is increasingly being embedded within the network itself, enhancing operations, optimization, fault diagnosis/recovery and security functions. The interaction between these roles is driving many of the architectural shifts observed today. Today, wide-area networks (WANs) must interconnect AI-intensive data center environments with distributed enterprise domains, effectively bridging heterogeneous traffic models and service requirements.

AI-Driven Changes in Traffic and Risk:

AI is reshaping both the structure of network traffic and its associated risk profile. From a traffic perspective, flows are becoming increasingly east–west, bursty, and machine-generated, with reduced visibility due to encryption and abstraction layers. From a security standpoint, AI introduces new classes of actors (e.g., non-human identities and autonomous agents), as well as new attack vectors, including adversarial AI and data exfiltration via model interactions.

These shifts are tightly coupled. The same properties that define AI-driven traffic—distribution, dynamism, and opacity—also complicate detection and enforcement. As a result, security architectures are evolving toward:

• Identity-centric models that extend zero-trust principles to non-human entities.

• Data loss prevention mechanisms adapted to AI-generated and AI-consumed data flows.

• Fine-grained segmentation within network fabrics, subject to latency constraints.

• Increased reliance on AI-driven detection and response systems to counter AI-enabled threats.

Importantly, these dynamics vary across network domains (LAN, WAN, and data center/cloud), requiring domain-specific adaptations while maintaining consistent policy frameworks.

Alignment with “Why Batch Pipelines Break AI Agents: The Case For Streaming-First Network Operations:”

The key points made in this article are highly consistent with the above referenced IEEE Techblog post written by Shazia Hasnie, Ph.D.  Both articles treat AI as an architectural forcing function: Shazia’s article focuses on the data/telemetry layer, while this post extends the same logic to the broader enterprise network stack. The core claim in both pieces is that legacy architectures were built for human-operated, latency-tolerant workflows, not autonomous AI systems. In the Shazia’s article, batch pipelines fail because they deliver stale, incomplete, and inconsistent context to AI agents.  Here, the same mismatch appears at the network level, where legacy enterprise designs were optimized for north–south traffic, perimeter trust, and static operational assumptions. Both arguments are fundamentally about architectural mismatch rather than isolated product shortcomings.

A particularly strong point of overlap is the emphasis on real-time context. Shazia’s article argues that AI agents require continuous data freshness and an ordered event stream to function safely, while this piece frames AI networking as a shift toward machine-timescale traffic, streaming telemetry, and identity-aware enforcement. In both cases, the network is no longer just a transport layer; it becomes part of the control loop that determines whether AI decisions are accurate and timely.

The failure models are also similar.  Shazia identifies five failure modes of batch-to-agent mismatch: stale data, memory gaps, delete blindness, schema fragility, and coordination failure. While not using that taxonomy explicitly, we share the same underlying diagnosis by arguing that complexity, fragmentation, and legacy operational models are now the primary sources of risk. Our discussion of east–west traffic, non-human identities, zero trust, and observability mirrors Shazia’s broader point that autonomous systems fail when their surrounding infrastructure cannot preserve state, sequence, and policy consistency.

These two articles work well together because they address different layers of the same transition. The first article is mainly about the data plane of AI operations—how telemetry, event streams, and agent inputs must move from batch to streaming to avoid operational failure. This article is about the network and security architecture around that data plane—how the enterprise stack, LAN, WAN, and fabric must evolve to support AI-native workloads and enforcement.  Hence, the reader can consider the two articles companion pieces.

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About the Author:

Shashi Kiran has nearly 30 years of experience in network, security and cloud technologies, primarily as an operator and executive in public and private B2B companies, where he has held global product management and marketing positions. He’s adopting a protopian view of AI, while being both fascinated and frightened by it at the same time.

Shashi is currently the CMO at Nile, whose network architecture aligns with what AI-era networks require: identity-centric control, embedded security, and autonomous operations.  He previously held executive roles at Cisco, Check Point Software, Broadcom and other venture backed startups, and is based in San Jose, CA. He can be reached at http://www.linkedin.com/in/skiran

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References:

Why Batch Pipelines Break AI Agents: The Case For Streaming-First Network Operations

Nile launches a Generative AI engine (NXI) to proactively detect and resolve enterprise network issues

Fiber Optic Networks & Subsea Cable Systems as the foundation for AI and Cloud services

Dell’Oro: Bright Future for Campus Network As A Service (NaaS) and Public Cloud Managed LAN

Cisco Plus: Network as a Service includes computing and storage too

https://nilesecure.com/press-releases/networking-and-security-in-higher-ed

https://nilesecure.com/press-releases/nile-powers-black-hat-mea-2025-with-zero-reported-incidents

Overview:

For the telecommunications industry, many pundits say 2026 will be the year of “AI Ontology [1.],” primarily because a standardized knowledge plane is now seen as the “ultimate driver” for reaching higher levels of network autonomy. Industry experts from companies like Telstra and Amdocs emphasize that for agentic AI to move from isolated pilots to enterprise-scale operations, it requires a structured, explainable, and typed world model—an ontology—to unify data across fragmented systems.

Note 1. An ontology in AI is a formal, machine-readable framework that defines the concepts, properties, and relationships within a specific domain to enable knowledge sharing, reasoning, and semantic understanding. It structures data into a network of “things” (classes) rather than just files, acting as a “Rosetta stone” that allows AI systems to understand context, infer conclusions, and act on data.

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Several network providers are adopting a “standardized, ontology-driven knowledge plane” to enable agentic AI to operate across traditionally siloed network systems. This shift in 2026, is driven by the need for Level 4 and 5 network autonomy, where agents require a common language to reason about network states and business intents.   Here’s a recap:

1.  Mark Sanders, Telstra’s chief architect, talked about the emergence of a structured, explainable knowledge plane that removes silo barriers between agents, freeing them up to become the workhorses of network automation. “We think for the autonomous network to reach level four or five is going to require a standardized, ontology-driven approach on the knowledge plane,” said Sanders at a recent Ericsson conference, touting this approach as the ultimate driver in next-level autonomous networks.

2.  For BT, agentic AI is already yielding tangible results in IT service desks, especially as organizations shift from assistance to execution, according to Girish Mahajan, senior leader for mobile AI data/automation. In particular,  AI agents have reduced trouble ticket resolution times. “It has reduced the time of the manual effort, and it has also increased efficiency of the service desk,” he said.  However, same autonomy that drives value also introduces unpredictability.

“The outcome of agentic AI is something unpredictable because it’s continuously adapting during execution,” he said, adding a call for better design principles. “We need reflection-based architecture, and we need better AI/human collaboration. AI agents should learn from their actions and should work along with humans in their day-to-day.”

3. For Vodafone, work has revolved around lighthouse projects: small-scale efforts to demonstrate the value of a larger business use case.

“It’s quite a mundane use case around energy cost recovery. So obviously, energy is a huge operational expense for our industry,” said Simon Norton, digital/OSS engineering director, Vodafone Group. “It’s very complex, especially when you’re working in that multi-market environment, to manually compare line by line with energy bills against your own data sets.”

Vodafone’s AI agents, therefore, have been automatically ingesting bills and comparing them to identify any tariff anomalies.

“It’s mundane but actually super valuable,” said Norton, who stressed operators should find a project with a clear value proposition and get it out into production quickly. “You build the credibility, you start to get the funding into the system, and it buys you the time to work on that longer-term strategy.”

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• From Assistant to Doer: AI is evolving from a “helper” that provides insights to a “doer” that autonomously observes, decides, and executes actions within governed boundaries.
• Multi-Agent Orchestration: 2026 will see the rise of coordinated multi-agent ecosystems. These systems require an ontology to ensure that a “planner agent” can accurately break down goals for specialized “worker agents” without semantic confusion.
• Intent-Based Orchestration: To ensure network stability, telcos are adopting intent-based orchestration layers. These layers use ontologies to provide the deterministic, model-driven framework necessary to ground agent actions in real-world business intent.

• Network Autonomy: CSPs are aiming for TM Forum Level 3 or 4 autonomy by late 2026, using agents to turn intent into outcomes in live networks.
• Operational Leverage: Rather than massive headcount cuts, agentic AI is providing “operational leverage,” allowing teams to manage growing network complexity with the same workforce.
• Measurable ROI: Investments are focusing on high-impact areas like autonomous incident handling (30-40% cost reduction) and predictive maintenance (up to 40% fewer outages).

• Structured Knowledge Plane: Operators are shifting toward a standardized, ontology-driven knowledge plane to remove silo barriers between agents. This allows multiple specialized agents to collaborate on “broader, bigger outcomes” like root cause analysis across billing, CRM, and network systems.
• Enabling Agentic Autonomy: While 2025 focused on “agentic AI” as a buzzword, 2026 is about the foundational infrastructure—specifically graph-based data systems and digital twins—that gives agents the “executable semantics” they need to plan and act safely.
• Unified Truth for Agents: Without a central ontology, horizontal AI platforms often suffer from “agent drift,” where different agents interpret the same business logic (e.g., “unlimited plan”) differently, leading to billing and provisioning errors.

Ericsson’s View:

Hassan Iftikhar, Ericsson’s head of product domain data & analytics,  called for better hyperscaler collaboration on scale, foundational cloud, and AI capabilities.

“The AI tooling, the security framework, we use those to industrialize and put agents into production… It’s pretty much an ecosystem that works together,” he said. At the panel, the data head revealed the vendor’s role in the agentic ecosystem through the use case of one operator needing help with catalog management, as well as scarce developer skills.

“They wanted to take the pain out of product configuration. So we designed a multi-agentic system where it basically helps product managers and marketers to configure and publish new instances through an actual language. So very complex catalog engineering, which can take weeks, is reduced to hours where you can search for reuse and launch.”

Iftikhar also revealed an OSS tool to help one operator’s engineers to diagnose and resolve issues within their operational instances – resulting in an agent that was seemingly too autonomous for the client.

“We put this use case together, basically taking an intent from an operations engineer, such as data diagnostics, and into it, we built the ability to take remediation actions automatically. What we sort of decided from that was a bit of a step too far to just throw that to an operations department for it to autonomously take steps. So we actually had to go in and build guardrails to limit that capability to a human oversight capability.”

“I think what we learned is that we have to sort of build that confidence in the team step by step before we can actually go to fully autonomous operation. Our learning from adjusting that use case was to be practical and adapt very quickly to what the business really needs.”

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References:

https://www.sdxcentral.com/analysis/has-telco-already-faced-the-year-of-ai-agents/

The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core

Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation

Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks

Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)

AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products