SAP用更聪明，受管的Joule agents推进其自主企业

SAP用更聪明，受管的Joule agents推进其自主企业

SAP SE今天的宣布在其每年为开发者举行的TechEd会议上提出了一套人工智能创新，加强了它的Joule助手解决方案和Business AI平台，包括表格数据基础模型。

Joule Work的新能力将帮助AI agents提高效率，并与开放协议进行互操作。 在这种情况下，示范背景协议和Agent 2 Agent允许公司代理AI助理Joule收集背景，作出决定，并在整个企业执行多步骤的业务工作流程，同时带来第三方数据或服务。

然而，每一次agent工作时，它都需要像雇员一样由一个中央当局管理；它需要进入，它必须经许可在工具上执行。 SAP企业AI产品工程的副总裁兼首席产品官Ted Way在一次采访中告诉SiliconANGLE

Way解释说，SAP希望客户自己的agents在公司系统之间与其业务agents合作。 其提议是，开放这些互联还需要有一个有控制的切入点，使各组织能够强制执行和监督活动。 这就是一个新的管弦乐层将进入的地方，使助手和agents能够通过一个受管的网关引用Joule Agents.

此外，最近的更新将允许agents触发n8n工作流程，这是通过Joule Studio的重要连接器。 N8n是一个以节点为基础的工作流程自动化平台，可以连接不同的应用，并使用决定性的自动化；这意味着AI agents可以触发它，但与基因AI不同，n8n在决策时使用基于事件和触发的逻辑。 它不能产生幻觉；它不是随机的，将从一开始就遵守确切的规则，直至完成。

这意味着n8n适合高度规范或敏感的工作，这取决于确切的需求。

“它需要按这个顺序走这五个步骤

SAP的方法结合了灵活的自然语言代理触发器和规定的工作流程。 agent可能有助于确定何时需要，而遵守敏感程序仍需要精确的步骤顺序。

SAP利用云量数据为自主企业打地

今日公司宣布在SAP数据云中扩展SAP业务数据云肿瘤管理器，即BDC，通过让agents了解应用程序如何互相关联，为企业提供AI-原创作者体验。 它通过界定公司事实之间的关系来做到这一点，使agents能够更准确地说明理由，并在企业之间作出决定。

SAP收购Reltio，最近宣布在SAP Connect，带来在多域数据管理机上收购BDC. 这有助于将SAP和第三方系统的客户、供应商和产品记录统一为可信赖的应用数据和agents数据产品。

新产品旨在补充公司现有的知识图，该图从SAP数据中提供了丰富的商业背景。

此外，称公司正在扩展其与Dremio的湖屋能力，这是另一项收购，增加了对Apache Iceberg和Apache Polaris的本土支持，并增加了一个高性能、联合的查询引擎，可以不移动或复制数据而统一数据访问。

将AI模型带入表格

“并不是每个企业问题都是语言问题”,Way 称解释说，尽管AI方法大多侧重于自然语言，但许多商业数据都储存在表格中。

为解决这个问题，SAP 宣布其最新的表型AI模型SAP-RPT-2.0，扩展超越分类和回归支持异常和异常检测，通过嵌入提供AI动力分析。 这将提供多种能力，以减少人工工程，并从结构化数据中获得更快、更深入的见解。

该公司还采用了新的SAP-RPT-TS模型发布，这将有助于客户预测各种时间指数的数值，用于关键预测。 它使用SAP Business Planning而无需自定义的业务机器学习，允许技术程度较低的用户在他们的系统中部署模型。

它与惠普公司(SAP 宣布)合作，在国际空间站部署SAP-RPT。 目的是协助在故障升级前预测问题，从历史数据中重建缺失的传感器值，并为宇航员和地面团队编写可解释的任务健康报告，所有这一切都没有在分析前将数据送回地球。

图片：Microsoft设计师/SiliconANGLE

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SAP advances its autonomous enterprise push with smarter, governed Joule agents

SAP SE today announced a set of artificial intelligence innovations at its annual TechEd conference for developers, enhancing its Joule assistant solutions and Business AI Platform, including tabular data foundation models.

New capabilities in Joule Work will help AI agents become more efficient and interoperate with open protocols. In this case, the Model Context Protocol and Agent 2 Agent allow Joule, the company’s agentic AI assistant, to gather context, make decisions and execute multistep business workflows across the enterprise, while also bringing in third-party data or services.

However, every time an agent goes to work, it needs to be governed by a central authority just like an employee; it needs access and it must execute on tools with permissions. “Open protocols need governed doors,” Ted Way, vice president and chief product officer of SAP business AI product engineering, told SiliconANGLE in an interview.

Way explained that SAP would like customers’ own agents to collaborate with its business agents across company systems. The proposition is that opening those interconnections also requires a controlled entry point where organizations can enforce and oversee activity. This is where a new orchestration layer will come in, enabling assistants and agents to invoke Joule Agents through a governed gateway.

In addition, a recent update will allow agents to trigger n8n workflows, an important connector through Joule Studio. N8n is a node-based workflow automation platform that connects different apps and uses deterministic automation; this means that AI agents can trigger it, but unlike generative AI, n8n uses event-based and trigger-based logic for decision-making. It cannot hallucinate; it is not random and will follow exact rules from start to finish.

That means n8n is suitable for highly regulated or sensitive work that depends on exact needs.

“It needs to do these five steps in this order exactly like this every single time,” Way explained, so in that case, n8n is the go-to.

SAP’s approach combines flexible, natural-language agentic triggers with a prescribed workflow. An agent may help determine when it is needed, while compliance-sensitive procedures can still require an exact sequence of steps.

SAP builds on cloud data to ground the autonomous enterprise

The company today announced the expansion of SAP Business Data Cloud Ontology Manager in SAP Data Cloud, or BDC, to provide an AI-native authoring experience for businesses by giving agents an understanding of how applications relate to one another. It does so by defining relationships between company facts, allowing agents to reason more accurately and make decisions across enterprises.

SAP’s acquisition of Reltio, recently announced at SAP Connect, brings on board multidomain data management to BDC. That helps unify customer, supplier and product records across SAP and third-party systems into trusted data products for applications and agents.

The new product is designed to complement the company’s existing Knowledge Graph, which provides rich business context from SAP data.

Additionally, the company said, it’s expanding its lakehouse capabilities with Dremio, another acquisition, by adding native support for Apache Iceberg and Apache Polaris and a high-performance, federated query engine that enables unified data access without moving or copying data.

Bringing AI models to tables

“Not every enterprise problem is a language problem,” Way said, explaining that although much of the AI approach has focused on natural language, much business data is stored in tables.

To handle this, SAP announced its latest tabular AI model, SAP-RPT-2.0, which expands beyond classification and regression to support anomaly and outlier detection, providing AI-powered analysis through embeddings. It will give multitable capabilities to reduce manual engineering and deliver faster, deeper insights from structured data.

The company also launched a new SAP-RPT-TS model, which will help customers predict values across time-indexed metrics for key forecasting. It uses SAP Business Planning without custom business machine learning, allowing less technical users to deploy the model on their systems.

In a partnership with Hewlett Packard Enterprise Co., SAP announced it’s deploying SAP-RPT on the International Space Station. The aim is to assist with predicting issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission health reports for astronauts and ground teams, all without sending data back to Earth before analysis.

Image: Microsoft Designer/SiliconANGLE

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