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容联云全栈产品Agent化破解“Token黑洞” 为智能体效果付费_我的网站

A |     IT时报记者 郝俊慧          夏日炎炎，比天气更热的是AI。         7月20日，2026世界人工智能大会（WAIC 2026）的展馆里，有两个词到处可见：智能体和Token。    

     Lingjun Zhenwu M890 supernode instance  Photo: Courtesy of Alibaba CloudAlibaba Cloud on Tuesday officially launched its Lingjun Zhenwu M890 supernode instance in Ulanqab, North China's Inner Mongolia Autonomous Region, with the first batch of instances now available for sale in the region. 
The instance is designed to handle inference for mixture-of-experts models with up to 10 trillion parameters, the company said in a statement sent to the Global Times on Wednesday. 
This marks the first supernode-form computing architecture in China to successfully run large language models exceeding 2 trillion parameters, according to the company.
Industry expert Tian Feng told the Global Times that the commercial rollout of supernode infrastructure could significantly reduce training cycles, lower costs, and speed up iteration for AI developers requiring massive computational resources.
The company said the new instance has already been used to power commercial services for large language models such as KimiK3 and Qwen3.8Max.
The Lingjun Zhenwu M890 ­supernode instance supports FP8/FP4 low-precision computing. Through the ICNSwitch 1.0 chip, its scale-up interconnect scale has been expanded from 16 cards to 64 cards, with inter-card interconnect bandwidth boosted to 800 GB/s. Enterprises can provision 64-card, high-speed-interconnect computing units through the cloud without building their own data centers, according to the company.
In training scenarios such as autonomous driving and embodied intelligence, the instance delivers three times the training performance compared with the previous-generation Zhenwu 810E, the company said.
Ulanqab, where the supernode instance debuted, is one of Alibaba Cloud's five super data centers. The facility sources approximately 90 percent of its electricity from green energy, providing a low-carbon operating environment for high-density computing power.
Leveraging its climate, energy and network advantages, Ulanqab has transformed from "China's potato hometown" into the "token factory"  - a term increasingly used in the AI industry to describe infrastructure dedicated to producing the digital building blocks generated by large language models. 
By the end of 2025, the city had attracted 84 data center projects, including 81 intelligent computing centers, with total investment exceeding 500 ­billion yuan ($74.1 billion) and operational computing power reaching approximately 172,000 petaflops, ranking it firmly in the nation's top tier, according to domestic media reports.
On August 6, China's largest AI computing industrial park was completed and put into operation in Ulanqab. The project highlights a broader race in ­China to build massive AI data centers capable of supporting the next generation of AI models while addressing soaring electricity demand, according to Chinese experts.
In recent years, Inner Mongolia has been rapidly positioning itself as a global-scale AI computing center cluster. Major technology companies, including Huawei, Tencent, ByteDance and Alibaba; telecom operators China Mobile, China Telecom and China Unicom; as well as cyberspace infrastructure service provider VNET, have established computing facilities in the region.
As the AI industry gradually transitions from the training era to the inference era and large model parameters continue to expand, supernodes have become a central battleground for AI infrastructure.
Chinese vendors are accelerating deployments in this space. Huawei has commercially deployed more than 750 sets of its Ascend 384 supernodes across industries including internet, telecom operators, finance, education, healthcare, transportation and manufacturing. It is also the only domestic supernode to have trained state-of-the-art (SOTA) models.
Baidu AI Cloud has also launched its Tianchi 256-card supernode based on Kunlun chips, with support for major models including Wenxin, DeepSeek, GLM, and MiniMax.
Meanwhile, supercomputer manufacturer Sugon has unveiled China's first fully domestic 100,000-card AI ­supercluster Sugon 8000 (Dengfeng), integrating supercomputing and AI computing on a unified architecture. It has now been connected to the national supercomputing internet to provide computing services to government, research, and enterprise clients nationwide. 
Tian, former dean of SenseTime's Intelligence Industry Research Institute, told the Global Times that the flurry of domestic supernode launches reflects a broader inflection point as China's AI sector pivots from training capacity toward efficient, large-scale inference. 
The expert further said that the commercial viability of these systems - evidenced by Huawei's extensive deployed base, Baidu's rapid model adaptation and Sugon's integration into the national computing network - suggests domestic vendors are moving beyond proof-of-concept to genuine production-grade infrastructure, a prerequisite for sustaining the next wave of ­trillion-parameter model proliferation, Tian noted.
The move also underscores China's push for self-reliance in AI infrastructure as US chip export restrictions continue to tighten, Tian said, noting that, in the supernode domain, Chinese companies are shifting from imported graphics processing units toward homegrown interconnect chips and domestic compute clusters, a transition that could reshape value allocation across the AI industry chain.
。不少展台演示着类似的场景：某个智能体如何替人完成一段工作，比如读财报、写代码、打开网页……          然而，《IT时报》记者的一个困惑始终无法被解答：如何衡量这些智能体的价值？Token烧完之后，企业到底得到了什么？          “用Token收费，是大模型公司一种很‘狡猾’的做法，它不为自己的错误买单，你的钱还被花了。”2026世界人工智能大会期间，在容联云举办“协同进化·价值共生——企业级Agent的进化与商业化落地”的主题论坛上，亿欧董事长王彬坦言。

B |          容联云产研中心副总经理唐兴才引用的一组数据与之相印证：全球88%的企业已经部署了Agent，但其中不到10%的Agent真正为企业创造了可量化的业务价值。         论坛上，从Token定价模式到效果付费，从技术指标到业务闭环，围绕“价值”重新校准的AI评价体系成为嘉宾热议的话题。         当日，容联云宣布全栈产品Agent化，涵盖Voice Agent、质检Agent、辅助Agent、协呼Agent、CRM辅助Agent、智能客服Agent、私域运营Agent等完整产品矩阵，实现对企业营销、销售、服务、质检、运营全场景的纵深覆盖。

C |          Token黑洞 价值缺席          现阶段的企业级Agent往往有一个核心矛盾：Token在加速消耗，价值却持续缺席。         唐兴才描述了一个典型场景：企业引入AI编程工具后，如果不加以节制和规划使用，单个程序员的Token消耗成本可能是自身人力成本的10倍。许多企业初期以Token消耗量作为智能化程度的度量指标，直到财务报表出来，才发现成本膨胀与价值产出之间完全脱钩。

D |          在现有按Token消耗计费的模式下，AI服务方不承担产出质量风险，所有试错成本全部由企业端承担。         在商业模式中，这是一个典型的错位：供给方按消耗收费，消耗越多赚得越多，却与使用方的价值无关，优化Token成本的动力显然不足。         唐兴才也举例，一个语音Agent的意图识别准确率达到95%，但如果它无法推动订单转化、无法缩短工单了结时长、无法提升复购率，95%这个数字对企业经营者而言没有实际意义。         “关注业务闭环应关注的是业务指标，订单转化率、工单了结时长、运营覆盖人数与GMV贡献。”唐兴才指出。         但目前来看，技术指标和业务指标之间是断层的，需要工程化能力和行业Know-how来建立桥梁。         王彬将这一现象归结为评价体系的缺位。他提出VPT（Value Per Token）模型，主张以单位Token创造的商业产出来衡量AI的实际生产力，推动智能体从“Token消费者”向“价值生产者”跃迁。这一框架的核心逻辑是：Token的定价权不应掌握在算力提供方手中，而应由其对业务结果的实际贡献来决定。         效果付费 共担风险          当评价体系从技术指标转向业务指标，收费模式的重构就成为必然。         容联云解决方案中心总经理闫玉华在论坛圆桌环节透露，容联云今年正在探索一种新的业务模式——效果付费或成果付费，尤其涉及AI新技术和新场景，通过共担风险的方式化解客户的投入顾虑。         效果付费的前提，是Agent能够在特定场景中实现业务闭环，产出可被量化的业务结果。

E |          今年6月，容联云发布了Voice Agent。数据显示，在银行信贷催收与营销场景中，日均通话超过2万通，完成率94%。以可比口径计算，人工坐席月成本为5000至8000元，日均处理话务150至300通；Voice Agent月成本2000至3000元，日均处理话务量1万通以上，而且24小时运转，服务标准统一。

F |          当Agent的产出可以精确到通话量、完成率、回款金额这些业务指标时，效果付费就具备了可操作性的商业模式。         私域运营Agent则提供了另一个维度。一个人力运营人员管理几千到几万用户已是上限，而Agent可管理几十万乃至上百万用户，容联云的私域运营Agent已独立管理超8万私域客户，经营规模增长87%。

G |          在效果付费框架下，这一数字可以直接映射为GMV增量，成为定价的依据。         多点数智副总裁张宇的定价逻辑也很清晰，他以海外一个大超市客户为例，生鲜年销售额三四十亿元，损耗率8%至9%，通过补货智能体，哪怕损耗率只降低1个百分点，一年就节省数千万元，AI系统的投入成本相比之下几乎可以忽略。         如果Agent替代的是一个具体的岗位、承担的是一个具体的KPI，它的定价就不再取决于消耗了多少Token，而是取决于创造了多少可衡量的业务增量。         企业级Agent的价值锚点          企业级Agent的价值锚点究竟在哪里？          根据容联云这几年的实践，唐兴才总结认为，数据连通、人机协同、业务闭环缺一不可。         数据连通是价值锚点的地基。企业内部的Agent平台与CRM、ERP等业务系统长期割裂，Agent无法理解订单、合同、工单等业务对象的语义，也无法将作业过程中的知识与经验沉淀为可复用的资产。容联云的解法是在Agent与底层系统之间架设数据语义层，将业务数据抽象和实体化。

H |          业务闭环是价值锚点的度量刻度。唐兴才认为，企业级Agent有四个阶段的进化路径：单点能力（Copilot）、闭环任务、人机协作、自主业务闭环。前三个阶段，Agent的价值仍然依附于人的判断和执行；只有到第四阶段，Agent自主完成从订餐到信审的完整业务链路，其对业务结果的贡献才具备独立可量化的条件。         容联云将全栈产品Agent化，本质上是寻找那些可以实现业务闭环的场景，将Agent的考核从技术指标体系切换到业务指标体系。         行业Know-how是价值锚点的壁垒，多点数智在零售领域积累了10年的数字化底盘（Dmall OS）和智能中枢（D-BRAIN），因此，其Agent集群能够在选品、定价、补货、巡检等核心环节，输出可量化的经营结果。         简而言之，企业级Agent的价值不在通用模型的参数规模，而在垂直行业知识与业务流程的深度融合。         论坛上，嘉宾们形成的共识是，Agent 规模化核心突破口在于深度绑定企业客服、风控等核心业务，而技术幻觉、业务组织抵触是规模化两大核心壁垒。未来一年，多角色协同 Agent、全企业规模化部署将成为产业关键突破方向，合规与信任仍是长期普及的最大制约。

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