--- 格式版本: 2 标题: "Autonomy in Action: Achieving L4 Networks at Scale" 原文链接: "https://www.lightreading.com/network-platforms/autonomy-in-action-achieving-l4-networks-at-scale" 发布日期: "2026-07-15" 发布时间校准状态: "found" 发布时间来源: "llm:strict_original_body" 发布时间证据: "div class=Contributors-InfoWrapper: July 15, 2026" 发布时间校准原因: "该日期位于 Contributors-InfoWrapper 区域，且 nearTitle 为 true，符合文章发布时间的典型位置特征。" 发布时间校准置信度: "1" 发布时间候选数量: 80 发布时间严格候选数量: 22 发布时间原页读取状态: "原页面来自已抓取 HTML" 发布时间未找到原因: "候选日期无效或 LLM 未确认" 发布时间校准时间: "2026-07-18T11:50:53+08:00" 发现时间: "2026-07-17T15:04:52+08:00" 入库时间: "2026-07-18T03:57:20.032Z" 来源平台: "Tavily（AgentKey）" 搜索渠道: "tavily_web" 搜索词: "廖恒 华为 超节点" 匹配关键词: [] 相关厂家: [] 相关专家: [] 内容类型: "网页" 抓取工具: "AgentKey Scrape" 清洗工具: "AgentKey Markdown + LLM 正文裁剪" 原始附件: [] 图片摘要: - "✓ ./assets/img-84571d4c.webp | photo | ZTE高管在TM Forum演讲，展示Agent能力自演进架构，含反馈循环等三层模型。" - "✗ ./assets/img-0dd15455.webp | ad | 品牌Logo/广告" - "✗ ./assets/img-1168dd5c.webp | photo | 无关文章配图（Managed Services）" - "✗ ./assets/img-d4a87e73.webp | photo | 无关文章配图（Ericsson Network API）" - "✗ ./assets/img-86750342.webp | photo | 无关文章配图（Vendor exodus）" - "✗ ./assets/img-364d8732.webp | photo | 无关文章配图（Ericsson/Nokia AI-RAN）" - "✗ ./assets/img-f701c405.webp | photo | 装饰性配图/无关" - "✗ ./assets/img-37882d23.webp | photo | 装饰性配图/无关" - "✓ ./assets/img-47281d28.webp | other | " - "✓ ./assets/img-f52f0355.webp | other | " - "✓ ./assets/img-36228705.webp | other | " - "✓ ./assets/img-f4b551be.webp | other | " - "✓ ./assets/img-b8d82a25.webp | other | " - "✓ ./assets/img-f4c2bbb8.webp | other | " - "✓ ./assets/img-8da2f4b3.webp | other | " AI优质: "否" AI打分: 5 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "主题完全无关，全文讨论电信网络L4自治，未涉及超节点/AI Rack/机柜级AI基础设施、核心部件、供电、液冷等任何项目关注点。" AI质检模型: "deepseek-v4-flash" AI质检时间: "2026-07-30T01:03:56+08:00" AI主题相关性: 0 AI来源权威性: 3 AI新颖性: 2 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 0 采集批次: "2026年7月17日15点03分52秒" 采集批次ID: "20260717-150352-345" 去重键: "https://www.lightreading.com/network-platforms/autonomy-in-action-achieving-l4-networks-at-scale" --- AI and data are transforming telecom and other industries, sparking a determined move toward achieving true L4 autonomous networks. ![图片](./assets/img-84571d4c.webp) SOURCE: ZTE The rapid and massive uptake of AI and data in telecommunications and other industries is driving a concerted push toward true L4 autonomous networks, which might be much closer than some operators realize. That was one of the key takeaways at [TM Forum’s DTW 2026](https://www.tmforum.org/events/dtw/experience-dtw/new-for-2026), held June 23-25 in Copenhagen, and in particular a keynote address delivered by Ronan Dai, Deputy General Manager of Service & Data Intelligence Product at ZTE Corporation. Network autonomy is rated from L0 through L5, with L0 through L2 typically still requiring significant manual effort and processes in terms of O&M and other business processes and goals. L3 refers to partly autonomous networks in which systems can manage some tasks and processes on their own — often deployed for specific use cases — but require significant human oversight. In the L4 phase, a network can operate and maintain itself, with human decision-making only required for complex or unusual situations. Dai noted that substantial advances in AI models and data, and especially the emergence this year of agentic AI, are enabling rapid progress toward L4 network autonomy. AI agents are particularly well-suited to the concept of a network “thinking and acting” for itself, rather than waiting for human engineers. The recent growth of agent frameworks such as Hermes, OpenClaw and others further bolsters agent development and deployment. ### Operators have ambitious goals for autonomous networks While the majority of operators are still in the L0-L2 range, that appears likely to change considerably over the next several years and beyond. During the opening session of DTW Ignite, TM Forum released a new report, “ [Scaling to AN Level 4 to unlock network value](https://info.tmforum.org/2606_scaling-to-ANL4-to-unlock-network-value.html),” that includes some eye-opening highlights. The report, based in part on a survey of 80 global operators, found: - One in five (20%) operators expect to achieve L4 or above by 2027. - Meanwhile, 81% have set a goal of reaching L4 (or beyond) network autonomy by 2030. - Three-quarters (75%) of operators said they will increase their investments in autonomous networks this year. “The findings underline the race to 2030, as operators move from isolated automation gains toward scalable, commercial autonomous network models,” TM Forum said in a [news release](https://www.tmforum.org/news-insight/newsroom/autonomous-networks-leadership-forum-spotlights-level-4-shift). The report further notes that autonomous network strategies are evolving from cost-cutting measures toward revenue generation and growth, via next-generation customer experiences, service quality improvements and other means. ### 3 trends driving AN development and growth Those findings aligned with several critical trends that ZTE’s Dai described in his keynote. These include AI agent self-evolution, multi-agent collaboration and new progress in the Forward Deployed Engineering (FDE) delivery model, which enables scalable AI use and drives growth in L4 autonomous networks. Dai unpacked those three trends during his address: 1. Low-cost, controllable and secure agent self-evolution: In enterprise contexts, agentic AI is only as valuable as the organization’s ability to keep costs in check, secure its network against a wide range of security risks and retain ultimate control over business and technical operations. That’s no different for telco operators and other communications service providers (CSPs). ZTE advises a hybrid or “dual-track” approach in which centralized engineering teams improve the large base model(s). Meanwhile, lightweight technologies such as RAG, memory mechanisms, and workflow optimization are deployed locally, allowing agents to learn and adapt without retraining. This approach also helps minimize costs and dependencies, fostering speed and agility while still prioritizing security and compliance in sensitive environments such as telco networks. 2. Multi-agent collaboration enables breakthroughs: While single-agent approaches might be suitable for experimentation or pilot programs, they pose challenges at production scale because of “cognitive overload,” which leads to declining accuracy. Multi-agent approaches will be essential to solving that problem and other challenges that come with commercial scale, according to Dai. He noted that ZTE is playing a significant role in developing repeatable frameworks for multi-agent architectures in the industry, including the emerging A2A-T protocol. The multi-agent approach allows operators and other enterprises to decouple agent roles and skills, allowing for parallel processing and highly specialized agents with few limits on their learning and self-improvement. 3. FDE model transforms and optimizes on-site delivery: Finally, Dai explored how ZTE is innovating, emphasizing the forward-deployed engineering (FDE) model for AI and autonomous network delivery. In simple terms, the FDE model is built around business experts and AI experts embedded at client sites to better integrate service workflows and technical capabilities within the live operating environment. Dai said traditional, remote software delivery models can’t keep up with the frenetic pace of innovation and iteration in AI systems. Moreover, they’re unable to optimally meet the needs of specific operating environments, lending themselves to more “one-size-fits-most” implementations poorly suited for AI and autonomous network development. The FDE model is far better suited to improving system responsiveness and decision accuracy, enabling frequent updates and patching, and meeting dynamic commercial requirements. Dai called for cross-industry collaboration in key areas, such as establishing unified operational mechanisms, promoting development roadmap alignment and emphasizing open-source architectures and scenario-based practices repositories to ensure interoperability and repeatability at scale. [Learn more](https://www.zte.com.cn/global/solutions_latest/service_and_dgital_patform_01.html) about how ZTE can help your organization get closer to L4 networks at scale. ![图片](./assets/img-84571d4c.webp) Mar 04, 2026](https://www.lightreading.com/network-platforms/huawei-may-outlive-any-vendor-independent-tech-in-germany?recipe=popular-items&source_content_id=efcb249a9f8ef2fd990c6dd2750c0291) [![Network API revenues start to trickle, says Ericsson](./assets/img-d4a87e73.webp)\\ Feb 19, 2026](https://www.lightreading.com/network-platforms/network-api-revenues-start-to-trickle-says-ericsson?recipe=similar-items&source_content_id=2f21cbd540db49110620cee3f6f104f4) [![Telecom is suffering a big exodus of vendors](./assets/img-86750342.webp)\\ Jan 22, 2026](https://www.lightreading.com/5g/telecom-is-suffering-a-big-exodus-of-vendors?recipe=popular-items&source_content_id=aea01bf3465e8bf85d436f434f65284c) [![Ericsson and Nokia are diverging like never before on AI-RAN](./assets/img-364d8732.webp)\\ ![AI-powered RAN](./assets/img-47281d28.webp) Sponsored Content [Link to all document](https://www.lightreading.com/wireless/ai-powered-ran-moving-beyond-basic-automation "Link to all document") ![图片](./assets/img-f52f0355.webp)[5G](https://www.lightreading.com/wireless/5g) [Link to all video](https://www.lightreading.com/5g/ericsson-intelligent-automation-platform-expands-to-support-core-network-automation "Link to all video") ![图片](./assets/img-36228705.webp)[AI & Machine Learning](https://www.lightreading.com/digital-transformation/ai-machine-learning) [Link to all video](https://www.lightreading.com/ai-machine-learning/evolving-from-a-bits-transport-pipeline-to-the-tokens-connectivity-foundation- "Link to all video") ![Image reads '6G coming into focus'](./assets/img-f4b551be.webp) Sponsored Content [Link to all video](https://www.lightreading.com/6g/analyst-insight-6g-coming-into-focus "Link to all video") ![图片](./assets/img-b8d82a25.webp)[AI & Machine Learning](https://www.lightreading.com/digital-transformation/ai-machine-learning) [Link to all video](https://www.lightreading.com/ai-machine-learning/ai-and-agentic-ai-and-the-impact-on-wide-area-networks "Link to all video") ![AST SpaceMobile Bluebird launch September 2024](./assets/img-f4c2bbb8.webp) ![Fiber Connect 2026](./assets/img-8da2f4b3.webp)