This month at , we announced a wave of 麻豆原创 Business AI innovations all built on the same technology foundation that powers our that we are now delivering to our customers and partners, allowing them to add even more value in the future.

We showed how the future of enterprise software is built on an AI-native architecture, powered by 麻豆原创 app, data, and AI foundation. With this approach, we are enabling a platform shift across the tech stack in a non-disruptive fashion, empowering developers to work faster and smarter using the frameworks and tools of their choice.

麻豆原创 HANA Cloud and 麻豆原创 Business Data Cloud: powering our AI-native future

麻豆原创 HANA Cloud is the database for 麻豆原创鈥檚 AI-native software architecture and the foundation of our broader data fabric strategy. At 麻豆原创 TechEd, we announced new AI capabilities for 麻豆原创 HANA Cloud that spur AI innovation.  

For example, Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud is now generally available. This provides direct access to rich multi-model engines. Agents can be grounded in full enterprise data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings — all within a single in-memory engine.  

We鈥檙e also expanding 麻豆原创 HANA Cloud knowledge graph engine capabilities (Q1 2026) so customers can automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. What used to take weeks of manual modeling can now happen automatically in minutes. But that鈥檚 not all. We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can memorize past inputs and decisions — learning and remembering just like humans — and become continuously smarter.

These advances show that 麻豆原创 HANA Cloud is truly powering an AI-native future. .

Bringing together the power of 麻豆原创 BDC and Snowflake

We are bringing the power of Snowflake together with 麻豆原创 Business Data Cloud (麻豆原创 BDC), calling it 麻豆原创 Snowflake. This partnership enables zero copy data sharing with Snowflake via 麻豆原创 BDC Connect.

Enterprises already using Snowflake today can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 BDC, giving them seamless, real-time access to combined, semantically rich 麻豆原创 with non-麻豆原创 data in 麻豆原创 BDC. 麻豆原创 Snowflake will be made generally available in Q1 2026, and 麻豆原创 BDC Connect for Snowflake in H1 2026. Find more information here.

麻豆原创-RPT-1: a new category of AI models

One of our most exciting announcements at 麻豆原创 TechEd was the launch of our first enterprise relational foundation model 麻豆原创-RPT-1, pronounced: 鈥渞apid one.鈥�

Businesses run on structured data. But large language models (LLMs) struggle with a general understanding of table structures and associated semantics. This requires the use of machine learning, or 鈥渘arrow AI,鈥� for tasks like classification, regression, and more. But classical machine learning necessitates training a model on each task, which easily can lead to hundreds of separate models.

麻豆原创-RPT-1 puts them all into one single, pre-trained model that understands relational business data and predicts business outcomes. Unlike language, image, or video models, 麻豆原创-RPT-1 accurately predicts business based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk, and more.

We believe that 麻豆原创-RPT-1 is a super capable foundation model today. It provides up to 2x better prediction quality compared to narrow models and 3.5x better prediction quality as compared to LLMs. .

麻豆原创-RPT-1 comes in three versions. 麻豆原创-RPT-1-small is for super-fast predictions and 麻豆原创-RPT-1-large is for highest accuracy. Both will be generally available in Q4 2025 in the generative AI hub in AI Foundation. 麻豆原创-RPT-1-OSS is the open-source version, available in Hugging Face and GitHub.

You can test 麻豆原创-RPT-1 today with your data or our use case data samples via no-code UI or via API in the new 麻豆原创-RPT-1 playground, an intuitive and interactive space to test for free and open to everyone and .

We are continuously adding new capabilities to AI Foundation and models to the generative AI hub, empowering developers to experiment with orchestration tools and leading models to scale AI development and productization across 麻豆原创 and non-麻豆原创 environments. For example, Perplexity is now generally available in the generative AI hub, so users can correlate business data with external data from the internet. Evaluation Services and Prompt Optimizer, in close collaboration with NotDiamond, are now also generally available in AI Foundation, freeing up users to adopt the most appropriate model for their use cases without the need for rewriting prompts. .

Digital sovereignty made in Germany, for Europe

Digital sovereignty is becoming increasingly important, reflecting the need for regional AI services that align with local regulations, standards, and values. As an example, Europe will benefit from its own strong, trustworthy infrastructure to support innovation, data protection, and ethical AI.

AI Foundation, including various models and all the services we offer, is already available on our own cloud infrastructure. As a next step, we are expanding our 麻豆原创 Cloud Infrastructure offering in our 麻豆原创 data center in Walldorf, Germany, to Deutsche Telekom through the Industrial AI Cloud project, providing secure, high-performance infrastructure for AI innovations across public institutions, defense, and society. 麻豆原创 delivers 麻豆原创 Cloud Infrastructure, 麻豆原创 Business Technology Platform, and applications 鈥� including our AI Foundation with frontier AI from Mistral, Cohere, and others 鈥� on Telekom鈥檚 Munich data center. Both companies uphold the highest standards of data protection, security, and reliability.

This marks a milestone as more European companies join the Industrial AI Cloud project, advancing applied AI across Europe with trusted, business-embedded solutions that unlock the full potential of industry data. See the announcement here.

Enabling customers to build, extend, share, and orchestrate AI agents

To help manage Joule Agents and Joule skills, we have introduced the concept of AI Assistants 鈥� role-based AI teammates, accessed through Joule 鈥� like a financial assistant that brings together agents for cash collection, treasury, and more. We will provide AI Assistants in Joule for every core business role, offering our users an agentic experience like never before.

Out-of-the-box Joule Agents are powerful, but we know that every company has unique requirements. We believe AI should adapt to users鈥� systems, not the other way around, so we are enabling them to use Joule Studio to extend 麻豆原创鈥檚 pre-built agents with custom fields, tools, and reasoning logic while retaining all the deeply grounded integration capabilities 麻豆原创 provides. Joule Studio also provides low-code tools to build custom agents that integrate with all other Joule Agents, Joule skills, and 麻豆原创 BDC.

Using a low-code approach, users can build Joule Agents visually with natural language and drag-and-drop. But we also want to meet the needs of developers who want ultimate flexibility. Our pro-code approach gives developers the freedom to build agents using the agentic framework of their choice 鈥� for example, LangGraph, CrewAI, Google鈥檚 Agent Development Kit, and more. 麻豆原创 Cloud SDK for AI now supports agentic development, ensuring these pro-code agents can be seamlessly integrated and giving developers the best of both worlds: deep integration and full flexibility.

No matter how you want to build agents, an important question is how to integrate them into the larger ecosystem beyond 麻豆原创. We鈥檙e making Joule Agents fully compatible with the agent-to-agent (A2A) protocol soon, so agents can discover and collaborate with each other.

A2A exposes rich semantics describing an agent鈥檚 capabilities, allowing both 麻豆原创 and third-party agents to work together seamlessly. We are collaborating with partners 鈥� AWS, Google, Microsoft, ServiceNow, and more 鈥� to standardize this protocol for full interoperability. This capability will allow Joule to orchestrate tasks across multiple agents, both 麻豆原创 and non-麻豆原创, increasing automation and productivity across the enterprise. Read more here.

To manage and govern agents across the enterprise, is now generally available, providing centralized control of 麻豆原创 and non-麻豆原创 agents. In addition, is available now for tracing agent actions, benchmarking against KPIs, and identifying bottlenecks or opportunities for agents to further improve business.

No 麻豆原创 TechEd without ABAP news

The ABAP journey continues with 麻豆原创-ABAP-1, which will be available in the generative AI hub in Q4 2025. Trained on ABAP code, it is designed to build ABAP AI use cases, enabling developers to build smarter, custom AI solutions in modern ABAP code. .

In addition, ABAP Cloud development is coming to Visual Studio (VS) Code. The new ABAP Cloud extension for VS Code delivers a streamlined, file-based development experience with built-in AI assistance. Powered by an ABAP language server, it will initially support 麻豆原创 Fiori UI service development and expand to additional ABAP Cloud scenarios over time. This brings ABAP development into the same environment where developers already build with UI5 and CAP. General availability is planned for Q2 2026. .

What鈥檚 next: embodied AI and quantum

麻豆原创 TechEd is always an opportunity to look to the future. This year, that future includes not just humans, but also autonomous devices, including humanoid robots.

By integrating Joule Agents natively with robots, 麻豆原创 is bringing business logic into the physical world, enabling a wide range of autonomous devices to operate with enterprise context. We highlighted our strategic partnerships with robotics companies and system integrators to serve customers like Sartorius, Bitzer, and Matur Fompak, demonstrating how our expanding physical AI ecosystem enables robots to understand business processes and execute complex tasks autonomously.

Early proof-of-concept deployments show Joule successfully integrated with 麻豆原创 business applications and autonomous systems across asset performance, logistics, field services, and warehouse operations. While still in the pioneering stage, these implementations illustrate how 麻豆原创 is extending Joule to serve both human users and autonomous devices, shaping the future of enterprise AI.

Read more about the partnerships and implementations here.

AI is a new compute paradigm that changes everything. But there is another compute paradigm on the horizon: quantum computing. It鈥檚 early days, but 麻豆原创 is driving the future of enterprise computing with a vision to help businesses get ready for quantum computing.

麻豆原创 is not building quantum hardware; instead, we are focusing on creating quantum algorithms for business applications. These solutions are simple to deploy 鈥� on when needed, off when not 鈥� and are designed to be hardware-agnostic, collaborating with partners such as IBM to ensure seamless integration without re-platforming. This approach will enable organizations to unlock operational efficiency and drive better business results at enterprise scale.

I couldn鈥檛 be more excited about what鈥檚 next for our customers鈥� future as we bring 麻豆原创鈥檚 AI-native architecture to life.

Philipp Herzig is CTO of 麻豆原创.

We鈥檙e entering a new era, according to 麻豆原创 Executive Board Member Muhammad Alam, in charge of 麻豆原创 Product & Engineering: 鈥渢he era of agentic AI, where AI moves from being just a tool to becoming your trusted teammate.”

Joined on stage by 麻豆原创 CTO Philipp Herzig and Michael Ameling, president of 麻豆原创 Business Technology Platform, Alam set the stage during the kick-off keynote, saying: 鈥淎s developers, you are not on the sidelines of this AI revolution. You are the revolution. And we are here to supercharge you for what鈥檚 next.鈥�

Supercharging developers

Together they dismissed suggestions that AI will replace developers. 鈥淭he truth is that developers aren’t going away,鈥� Alam said. 鈥淭hey are getting supercharged. They’re becoming the architects of smart connected businesses. So, the real question isn’t if we need developers; it’s how fast can we empower them to thrive and lead in this AI-native era?鈥�

Ameling, Alam, and Herzig outlined 麻豆原创’s vision and highlighted the integration of AI, data, and intelligent agents to transform business processes and drive innovation. They also emphasized the importance of a unified data fabric and the deployment of advanced AI models and agentic technologies within the 麻豆原创 ecosystem.

In this new business environment, they noted that every enterprise is becoming a data company and every user experience鈥攆rom the front line to the boardroom鈥攊s becoming AI-driven.

鈥淎s developers, you don鈥檛 just code anymore,鈥� Alam said. 鈥淵ou also design intelligent workflows and supervise AI agents to shape real business outcomes.鈥� To help developers do this, 麻豆原创鈥檚 strategy centers on empowering them with applications, connecting them with data, and supercharging them with AI.

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麻豆原创 BTP is the foundation for AI agents

All this runs on 麻豆原创 Business Technology Platform (麻豆原创 BTP), which serves as the foundation for building and managing AI agents鈥攂oth 麻豆原创 built and custom developed. While 麻豆原创 BTP is the engine, the Business Transformation Management portfolio is the navigator, ensuring technology translates into real business impact. It aligns strategy, process, and people, turning AI-driven potential into sustainable transformation at scale.

Alam identified three broad themes during the keynote:

First, he said 麻豆原创 is continuing to make 麻豆原创 more open. As part of that, he announced 麻豆原创 Snowflake, which brings the full data and AI capabilities of Snowflake as a solution extension to 麻豆原创 Business Data Cloud (麻豆原创 BDC). This partnership is exactly what joint customers have been asking for, Alam shared.

Second, he said 麻豆原创 will provide developers with the most context-rich agentic platform, including the most deeply grounded set of ready-to-use agents that developers can customize to their needs. 鈥淭oday, you鈥檒l hear about creating custom Joule Agents using low-code and pro-code tools with agent builder in Joule Studio as part of 麻豆原创 Build鈥� Alam said.

鈥淲e鈥檙e also standardizing AI agent interoperability with the agent-to-agent protocol, allowing your agents to collaborate securely across ecosystems,鈥� he added.

Third, he announced 麻豆原创鈥檚 first foundation model built specifically for structured business data: 麻豆原创 RPT-1, pronounced 鈥溌槎乖� Rapid One.鈥� 鈥淥ur relational pretrained transformer delivers enterprise-grade accuracy and scale, outperforming both LLMs and AutoML for tabular AI, which is critical for building reliable high-value agents,鈥� Alam said.

鈥淚n short, we鈥檙e embracing an open ecosystem, supercharging agents with deep process and data context, and giving you the tools to amplify AI and agents,鈥� he said. As the keynote continued, Ameling and Herzig detailed these and other innovations, explaining how they will help developers work smarter and achieve more.

麻豆原创 BDC and Snowflake

Ameling expanded on the new partnership with Snowflake, which will bring Snowflake鈥檚 fully managed data and AI capabilities to 麻豆原创 customers. Together with the introduction of 麻豆原创 BDC Connect for Snowflake, he said this will result in cost savings and simplified data landscapes. 鈥淭his enables you to integrate 麻豆原创 and non-麻豆原创 data products seamlessly between 麻豆原创 BDC and Snowflake, so that you can deploy intelligent applications faster and share across your preferred data marketplace.鈥�

Ameling also positioned 麻豆原创 HANA Cloud as 鈥渢he database AI was looking for.鈥� With 麻豆原创 HANA Cloud and 麻豆原创 BDC, 麻豆原创 provides the best business data fabric to help address these challenges, he said. 鈥淲ithout 麻豆原创 HANA Cloud, you would have one database for each and every data representation, which leads to disaggregated data siloes that limit your AI potential. With 麻豆原创 HANA Cloud, we have all these powerful engines in one integrated, multi-model database.鈥�

Building on this, Herzig emphasized that every business requires a strong data foundation because 鈥淎I is nothing without well-organized data…On top of the data foundation sits our AI Foundation that allows you to not only use the latest frontier AI technologies out there in the market, but also to extend 麻豆原创鈥檚 out-of-the-box AI capabilities and build your own experiences deeply contextualized in your business processes and data.鈥�

麻豆原创 pioneers a tabular foundation model

Herzig then explained that 麻豆原创 RPT-1 was designed to address a crucial problem for developers and enable them to deliver much better predictive capabilities, enterprise-grade accuracy, and scale to business customers.

Until now, Herzig said, 鈥淲e still had to go back to good old machine learning 鈥o train what we call 鈥榥arrow鈥� AI models that are specifically made for each [business] task. Therefore, you really had to train a hell of a lot of models.鈥� For example, to solve 10 predictive tasks across 10 different entities like company codes or plants would require training 100 different models.

鈥淲hat we really want to do is get rid of all these models and just introduce one giant model that only requires a small amount of data to learn from,鈥� he said. That鈥檚 what 麻豆原创 RPT-1 is. 鈥淲e believe 麻豆原创 RPT-1 is the most capable predictive foundation model that鈥檚 out there today,鈥� he said, delivering much higher prediction quality while being very fast and super-efficient in terms of resource requirements.

Joule, Joule Agents, and AI assistants

The 麻豆原创 CTO also emphasized that the company is committed to providing developers with the most context-rich agentic platform. He noted that 麻豆原创 has already shipped 20 Joule Agents across lines of business and will have approximately 40 by the end of the year, and that these agents can leverage more than 2,100 pre-delivered Joule skills. In addition, more than 300 embedded AI scenarios across product lines are available for customers to date, including Joule Agents, growing to 400 use cases in total by the end of the year.

Ameling added: “Our promise is simple: build with intent. You describe the outcome and 麻豆原创 Build uses AI agents to generate code, logic, and UIs for you, all with seamless access to your applications and data while you stay in the flow… [麻豆原创] Joule for Developers enables vibe coding experiences to make intent-based development simple and intuitive.”

麻豆原创 is taking it one step further by providing extensions to work with VS code, Windsurf, Cursor, OpenAI Codex, Claude Code, Cline, and more directly in 麻豆原创 Build. “You choose a tool, and we meet you where you are,” Ameling said. He also announced that fine-tuned ABAP LLMs with ABAP 1 on AI Foundation will be published in Q4 this year.

麻豆原创 is redefining how developers interact with AI through innovations in Joule and agentic AI, but also in terms of physical AI. Herzig welcomed Torsten G. Mueller, Group CIO and COO BPS at Sartorius, to discuss how the partnership between Sartorius, NEURA Robotics, and 麻豆原创 is bringing to life robots that understand the what, when, and how based on live business context. 鈥淭his is how we鈥檙e really imagining the future, right? Humans and robots working in harmony through Joule, through AI,鈥� Herzig said.

Quantum computing

Looking to the future, Herzig ended the keynote by talking about another 鈥渃ompute paradigm that is still hard to seize鈥�: quantum computing. While he made it clear that 麻豆原创 is not building a quantum computer, 麻豆原创 is teaming up with quantum hardware leaders, like IBM鈥攚hose Director of Research and IBM Fellow Jay Gambetta joined via video鈥攖o help evaluate quantum computing for business processes and applications.

鈥淲e believe quantum will join classical and AI compute in your stack, and we鈥檙e embedding it into the processes and apps you鈥檙e already using, so it just shows up in the workflows of your enterprise,鈥� Herzig said. 鈥淎nd of course, with the cloud, we scale it all. Now, 麻豆原创 has got you covered, and you鈥檝e got your business covered.鈥�