IBM 将管理背景置于生产中心 AI
治理环境正在成为人工智能agents预期在整个企业中作出决定的实际要求。 随着企业超越个人生产力工具，这些agents需要能够反映所使用系统不断变化的条件的信息。
IBM公司的做法将数据基础与将AI投入生产这一更广泛的挑战联系起来。 大型语言模型的推理能力取决于它所获得的信息，使得数据传送成为业务关注，据IBM的Confluent和数据总经理Shaun Clowes(图片)称。
称表示，“你不会把应收账款工作交给在你的业务中从事能源生产的核物理学家。 “那么，为什么你会采取一个非常非常聪明的模型，给它所有关于你业务的可能信息，并说，“找出应收帐款？” 你需要合适的工作环境。”
Clowes在CUBE,SiliconANGLE Media的直播工作室独家播出时，与CUBE研究的Dave Vellante和John Furrier为IBM的AI操作模型访谈系列进行了交谈。 他们讨论了实时数据，agent专业化和生产治理，领先于IBM TechXchange 2026. (* 下文披露。 )
将管理上下文纳入数据流
企业数据架构围绕不断变化的存储，计算和应用要求而发展。 Agents引入了将旅行预订，行李跟踪和租车等跨业务系统的信息结合的需要，而根据克洛韦斯的说法，这些信息仍然与决定相关。
“现在我们需要一种新型的建筑，这种类型的建筑可以汇集我们企业的所有状况，“我们称之为小溪屋建筑。”
IBM Confluent 带来将流化，连接，处理，治理和查询能力结合在一起，支持这种方法。 Clowes解释说，该平台不要求首先将所有信息集中起来，而是支持在商业信息来源地的环境下编制可用的数据产品。
“我们的论点是，你实际上可以移动所有非常非常非常接近数据最初产生的地方，尽可能接近” “越早治理它越好。 你制作高值数据的速度越快越好然后，你只需要连接到所有相关的地方。”
提供专用的 agents 间
提供当前受管制的数据只是挑战的一部分。 Agents也需要适合他们工作的背景。 不同的团队可能使用相同的业务术语来表示不同的事物。 因此，根据克洛韦斯的说法，为agent准备上下文需要确定适用于其任务的定义。
“你可以描述，“嘿，在这个商业过程中，我的意思是X,” “然后，你就可以有这些规范的衡量尺度和这些规范的数据集，有效地代表模型的理由。”
企业可以从高价值任务开始，在获得经验时改进个人的agents. 一旦这些agents可靠地执行既定的业务流程，扩展便有机会审查工作本身是如何组织的，克洛韦斯对此作了解释。
“然后，你可以对监管的agent说：`我甚至需要这些过程吗？我应该做完全不同的事吗？“关于整个系统最好的一点就是它可以在每一层学习。”
提供生产方面的商业环境
随着agents承担更多的责任，获取当前商业信息变得日益重要。 从人力援助决定转向自主经营改变了企业必须提供信息的方式。 持续工作的Agents需要访问操作数据而不等待一个人收集，克洛韦斯解释说。
“人们曾经把数据说成是新石油，但他们真正的意思是，从某种意义上说，数据是存在的，并且被人类用来做出正确的决定。” “企业是由人类经营的。.....这就是你如何获胜。”
信息可能来自石油钻机、游轮或其他远程系统。 根据克洛韦斯的说法，企业必须将这些来源连接到一个可用的操作视图中，而不假定一个集中解决方案将包含一切。
“你应该总是问自己，`我怎样才能知道我的生意状况？' “因为这是你们作出决定的世界。 这就是人类也会作出决定的世界。”
以下是SiliconANGLE的完整视频访谈
(*披露：CUBE是IBM的AI操作模型访谈系列活动的付费媒体合作伙伴。 无论是CUBE事件报道的发起人IBM还是其他发起人，都没有对CUBE或SiliconANGLE上的内容进行编辑控制。 ).
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IBM puts governed context at the center of production AI
Governed context is becoming a practical requirement for artificial intelligence agents expected to make decisions across a business. As enterprises move beyond personal productivity tools, those agents need information that reflects changing conditions across the systems they use.
IBM Corp.’s approach connects that data foundation to the broader challenge of putting AI into production. A large language model’s reasoning capabilities depend on the information available to it, making data delivery an operational concern, according to Shaun Clowes (pictured), general manager of Confluent and Data at IBM.
“You wouldn’t give your accounts receivable work to a nuclear physicist that worked on your energy production side of your business,” he said. “So, why would you take a very, very smart model, give it all of the possible information about your business and say, ‘Figure out accounts receivable?’ You need the right context for the job.”
Clowes spoke with theCUBE Research’s Dave Vellante and John Furrier for IBM’s AI Operating Model interview series, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed real-time data, agent specialization and production governance ahead of IBM TechXchange 2026. (* Disclosure below.)
Building governed context into the data flow
Enterprise data architectures have evolved around changing storage, computing and application requirements. Agents introduce a need to combine information across operational systems, such as travel bookings, baggage tracking and rental cars, while that information is still relevant to a decision, according to Clowes.
“Now we need a new type of architecture, a type of architecture that can bring together all the state of our business right now and present that directly to LLMs,” he said. “We call that the streamhouse architecture.”
IBM Confluent brings together streaming, connectivity, processing, governance and querying capabilities to support that approach. Rather than requiring all information to be centralized first, the platform supports the preparation of usable data products can happen across the environments where business information originates, Clowes explained.
“Our argument would be that you can actually move all of that very, very close to where the data is first produced, as close as you possibly can,” he said. “The earlier you govern it, the better. The quicker you produce the high-value data, the better. Then you need to just connect up to all the relevant places.”
Giving specialized agents room to reason
Delivering current, governed data is only part of the challenge. Agents also need context tailored to the work they’re performing. Different teams may use the same business term to mean different things. Preparing context for an agent therefore involves establishing the definitions that apply to its task, according to Clowes.
“You can describe, ‘Hey, in this business process, what I mean is X,’” he said. “Then, you can have these governed metrics and these governed datasets that effectively represent the foundation upon which the model will reason.”
Enterprises can begin with high-value tasks and improve individual agents as they gain experience. Once those agents reliably execute established business processes, the opportunity expands to examining how the work itself is organized, Clowes explained.
“Then you can say to the supervising agent, ‘Do I even need these processes? Should I just do something totally different?’” he said. “The best bit about this whole system is it can learn at every layer.”
Making business context available in production
As agents take on more responsibility, access to current business information becomes increasingly important. Moving from human-assisted decisions to autonomous operations changes how businesses must deliver information. Agents working continuously need access to operational data without waiting for a person to gather it, Clowes explained.
“People used to talk about data as the new oil, but what they really meant was [that] data, in some sense, existed and was used by humans to make good decisions,” he said. “Businesses were run by humans … [and] that’s how you win.”
Information may originate on oil rigs, cruise ships or other remote systems. Enterprises must connect those sources into a usable operational view without assuming one centralized solution will contain everything, according to Clowes.
“You should always be asking yourself, ‘How can I know the state of my business right now?’” he said. “Because that is the world in which you make decisions. That is the world in which humans would make decisions, too.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of IBM’s AI Operating Model interview series:
(* Disclosure: TheCUBE is a paid media partner for IBM’s AI Operating Model interview series event. Neither IBM, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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