The Fundamental Difference Between Siemens Xcelerator and an Ordinary Software Marketplace
The fundamental difference between Siemens Xcelerator and an ordinary software marketplace is this: a marketplace is designed to “sell products,” whereas Siemens Xcelerator is designed to “help products continuously evolve in real-world industrial settings.”
By Tian Yanlin, reporting from Aofeisi
Seriously, who can still work without AI these days?
Ask AI a quick question while writing a document, have it generate a report automatically, or let it organize background information before following up with a customer. Even tasks such as financial reconciliation and sales scripting are now being handled behind the scenes by AI employees.
To be honest, the wave of AI employees that major internet companies have been developing over the past six months has already quietly become part of everyone’s daily routine.
According to third-party statistics, in June this year alone, mainstream desktop AI office agents recorded 60 million visits.
These agents are certainly impressive: they can write documents, create reports, follow up with customers, assist with sales decisions, and even screen high-potential projects for investment firms and generate analytical reports.
But if you put these smart agents directly into a factory workshop, chances are they would accomplish nothing.
The 2025 Report on the Current State and Future Trends of Industrial Agent Applications (hereinafter referred to as the “Report”) surveyed more than 200 manufacturing companies. The findings are sobering:
43% of companies have not yet deployed industrial agents, while only 8% have achieved widespread adoption.
It is not that companies do not want to use them—63% of companies are held back by deployment costs, while 46% cannot find people who understand both technology and production lines.
Of course, factories also face obstacles such as equipment spanning multiple generations and a bewildering variety of protocols. A longstanding data divide separates information technology (IT) from operational technology (OT), while data security must also remain independently controllable. All of these factors stand in the way.
Ultimately, industrial operations have never been a matter of isolated problems. They are a systems engineering challenge spanning R&D, engineering, manufacturing, quality, and operations and maintenance.
Optimizing individual points cannot solve the whole system. This is why the bar for industrial-grade agents is far higher than for office applications.
Industrial agents need to understand industrial semantics, call industrial tools, connect to real-time data, and execute tasks within closed-loop workflows.
In other words, all of their capabilities must be built around the way an enterprise actually operates. Simply wrapping a large model in a superficial application is not enough.
For companies in urgent need of industrial-grade agents, developing everything from scratch behind closed doors is neither practical nor affordable.
As generative AI and agent technology accelerate into the industrial sector, Siemens is further connecting its existing capabilities in industrial software, automation, data, and ecosystems.
On the one hand, it is launching AI-native products such as the Eigen Engineering Agent. On the other, through Siemens Xcelerator, it is bringing together products and partners to drive the large-scale adoption of industrial AI.
Siemens’ Self-Developed Industrial Agents: From Assisted Recommendations to Autonomous Execution
The Eigen Engineering Agent, which is now fully available for the Chinese market, received the “SAIL Star” award at WAIC last month.
It is Siemens’ first AI agent designed for industrial automation engineering.
Unlike common AI-assisted tools, the biggest difference with the Eigen Engineering Agent is that it can independently plan, execute, and validate tasks end to end within real engineering systems.
Previously, electrical engineers would complete wiring and hardware design in ECAD tools, while automation engineers had to write programs based on an entirely different description system.
Equipment lists, variable tags, and control logic had long depended on repetitive manual entry. Whenever hardware changed, the software had to be reconciled again—a time-consuming process prone to errors.
The Eigen Engineering Agent now includes ECAD integration and automatic project generation.
It can read electrical design files in mainstream formats such as XML and AML, identify data conflicts, configure connections, and generate PLC variable tags based on the actual hardware topology.
Engineers can also describe workstations, supporting equipment, and operating logic in natural language, generating projects within minutes that comply with industry standards and remain open for further development.
The real-world results are equally impressive.
In practical applications, the Eigen Engineering Agent has improved execution efficiency by 2–5 times compared with manual workflows, increased engineering efficiency by up to 50%, and improved overall solution quality by 80%.
The agent has now been deployed at more than 100 companies across 19 countries and regions worldwide.
In China, the Eigen Engineering Agent has also begun pilot projects with multiple companies.
For example, Zhongke Motong uses it for intelligent EMB assembly equipment for new-energy vehicles. The solution has reduced both program development time and on-site commissioning cycles by 30%, while cutting labor and material waste by 10%.
Of course, the Eigen Engineering Agent was not developed to replace human engineers.
Quite the opposite: it handles mechanical and tedious tasks such as repetitive coding, drawing interpretation, and equipment configuration, freeing engineers to focus on higher-value system decisions and solution innovation.
The Eigen Engineering Agent is just one product in Siemens’ portfolio of self-developed industrial agents.
By integrating Graph Studio, AI Studio, and the Mendix low-code platform, Siemens has also launched Intelligence Center X (ICX), an industrial AI orchestration software.
It is not a large model. Instead, it is an AI orchestration layer built on top of an enterprise’s existing systems: connecting downward to PLM, ERP, MES, CRM, and OT field data, while managing upward the models, agents, and workflows in a unified manner. It also tracks every data access and agent decision.
Put simply, ICX is an “AI control center” for factories, provided by Siemens.
It does not replace a factory’s existing systems. Instead, it sits above them, organizing data, knowledge, models, and agents so that AI can move from merely “being able to chat” to “being able to get things done,” while remaining manageable and traceable. Ultimately, it helps factories save time and costs and improve efficiency.
As a new development in Siemens’ agent capabilities, ICX provides modules for building enterprise-level knowledge graphs; creating data analysis and machine learning models; developing Skills; building and debugging Agents; and orchestrating multi-step tasks through Workflows. It supports the development, debugging, and testing of AI agents in the cloud.
All of this demonstrates that industrial AI can indeed get work done. But unlike internet applications, industrial scenarios do not have relatively standardized requirements.
An automotive factory, a semiconductor plant, and a data center face completely different production processes and equipment systems.
Even within the same factory, the needs of R&D, production, quality, and operations and maintenance vary enormously.
The real challenge for industrial AI is not to build an omnipotent super-agent, but to adapt mature agent capabilities to the automation needs of different industries, domains, and scenarios.
Industrial AI Needs More Than Just Agents
Siemens has not stopped at individual agent products. Instead, it has modularized its accumulated industrial capabilities to create Siemens Xcelerator, an open digital business platform.
Traditional digital platforms often follow a marketplace logic: the buyer-seller relationship ends the moment a transaction is completed.
Siemens Xcelerator aims to serve as “the launchpad for industrial AI”:
Through product portfolios and a partner ecosystem, it connects the software, automation, data, and industry expertise required by industrial AI, helping customers identify scenarios and match solutions—and move beyond acquiring products toward deployment and value realization.
On this platform, Siemens has established a closed loop for industrial AI through three clearly defined layers.
Layer One: Product Portfolio
The reality is that the biggest obstacle facing many manufacturing companies is not a lack of understanding of AI’s value, but a lack of the capabilities needed to develop solutions from scratch.
Siemens Xcelerator first provides industrial AI solutions that can be deployed directly.
For example, products such as the Eigen Engineering Agent, which have been validated in real engineering environments, can be embedded directly into engineering and production processes, allowing companies to get up and running quickly.
Siemens’ various self-developed agents are deployed on the Siemens Xcelerator platform, enabling companies that need them to use them out of the box.
Layer Two: Development Ecosystem
Companies that do not want to use an off-the-shelf product and instead prefer to build their own agents can do so as well.
Siemens Xcelerator provides developers and ecosystem partners with an industrial agent development kit.
The kit includes core components such as Skill Creator, Agent Framework, and Workflow, supporting enterprise users in developing, debugging, and testing AI agents in the cloud.
Specifically, it includes AI knowledge retrieval and Q&A services based on RAG (retrieval-augmented generation), as well as Skill and workflow generation services based on the Skill Creator and Workflow tools, helping companies turn software and internal capabilities into agents.
Most importantly, it packages OT-level engineering know-how—including PLCs, industrial edge computing, data acquisition, and anomaly analysis—into Skills that agents can understand, interpret, and call directly.
In essence, this is about giving industrial expertise “an interface.” That is also Siemens’ unique advantage.
The entry point can change, but the underlying knowledge and processes can become a long-term corporate asset.
Siemens’ ECX Agent for energy and carbon management was built using these development kits.
It provides natural-language human-machine collaboration. Under user supervision, it can independently perform business tasks such as professional energy-efficiency management, intelligent equipment operations and maintenance, and carbon-data MRV (monitoring, reporting, and verification).
It uses the Siemens Xcelerator intelligent-agent application development framework and an underlying industry-specific large-model engine to implement intelligent energy and carbon management workflows.
It then calls the APIs of Siemens’ Smart-ECX energy and carbon management software platform through tool interfaces, reads real-time energy-consumption and carbon-emissions data, conducts carbon inventories and energy-and-carbon audits, answers energy- and carbon-related questions, and outputs emissions-reduction plans.
In addition to internally developed cases, Beijing Zhidian Interactive and Shanghai Quanxiao Information Technology have also used Siemens’ development kits to configure industrial AI agents suited to their own needs.
Zhidian Interactive reused the industrial agent development kit on Siemens Xcelerator through APIs, using AI knowledge-base capabilities for document parsing and vector retrieval.
The company then used its own platform to complete agent orchestration, business processes, human-machine approval loops, and end-to-end governance, packaging the retrieved industrial know-how into reusable Skill-based digital workforce assets.
Quanxiao Information Technology, meanwhile, uses the Siemens Xcelerator industrial agent development kit across its entire workflow, directly reusing native capabilities such as knowledge bases, Skill generation, and Agent orchestration.
The company focuses on specialized manufacturing scenarios and conducts secondary development by incorporating industry-specific business rules. It delivers industrial agent solutions tailored to specific industries, such as automotive OBD testing agents and equipment operations and maintenance agents.
Layer Three: Commercial Access
Getting an agent to work in a laboratory only accomplishes the transition from zero to one.
Once an agent has been developed, enabling it to find buyers and be replicated at scale becomes the central challenge of commercialization.
On the Marketplace online platform within Siemens Xcelerator, customers can more quickly find vetted industrial AI capabilities.
Ecosystem partners can also showcase their products, reach industrial customers, receive feedback, and continuously iterate.
This is also where Siemens demonstrates its commitment: even third-party self-developed agent vendors are welcome to join the Siemens Xcelerator platform.
AI innovators can apply to join the Siemens Xcelerator ecosystem. Once approved, companies can list their offerings on the platform, deliver differentiated industry solutions, and complete project delivery for customers.
For example, Aqrose Technology can connect its self-developed AQ-VLM industrial vision foundation model and VisionAgent visual application platform to the Siemens X Data Hub data foundation and Teamcenter PLM system through standard APIs—without having to restructure its technology stack.
Siemens provides industrial data context and access to business processes, while Aqrose Technology contributes its system-validated core capability for visual defect detection.
The “multidimensional industrial AI vision foundation” jointly created by the two parties undergoes interoperability testing and security and compliance reviews before being listed on the Marketplace, where it provides more manufacturing companies with out-of-the-box intelligent quality inspection capabilities.
In addition, Xulxu Technology, a Siemens Xcelerator ecosystem partner, has developed an AI-powered automatic drawing-generation platform that can quickly convert 3D models into 2D engineering drawings, helping companies improve delivery efficiency.
The results are easy to see. Some automotive equipment and non-standard automation companies found Xulxu Technology’s solution on the Marketplace. After adopting it, their per-person drawing output time fell from several days to just hours, saving a 10-person team more than 7,000 working hours annually.
Today, the Siemens Xcelerator platform covers industries including automotive, food and beverage, electronics and semiconductors, data centers, and green buildings.
During WAIC 2026, nine more AI partners signed ecosystem cooperation agreements with Siemens Xcelerator, further expanding its coverage of physical AI, embodied intelligence, industrial agents, and computing-power scenarios.
As of July 2026, Siemens Xcelerator had brought together more than 900 digitalization and decarbonization products and solutions, with over 600,000 registered users and more than 600 ecosystem partners. More than 100 of those partners had already listed AI industry applications.
Its purpose is not to provide the industrial world with a formulaic standard answer. Instead, through the Marketplace, curated business portfolios, and an open ecosystem, it is building a vast self-evolving system capable of continuously generating new answers based on on-site requirements.
How Siemens Xcelerator Replicates One Success Across More Factories
Taking a step back, what truly determines the value of Siemens Xcelerator is not how many products are displayed on the platform or how many partners it brings together, but whether these resources can actually work together.
Scaling industrial AI does not mean copying and pasting the same agent into 100 factories. It means turning every successful delivery into the starting point for the next deployment.
Self-developed products such as the Eigen Engineering Agent are first piloted in real engineering systems, demonstrating that industrial agents can not only understand tasks and generate solutions, but also enter PLC, HMI, and automation engineering workflows, complete end-to-end execution, and deliver measurable ROI on production lines.
Behind this is Siemens’ accumulated industrial expertise, which provides the foundation.
The engineering knowledge embedded in TIA Portal, PLCs, and industrial edge systems determines whether an agent can understand industrial semantics, connect to real equipment, and operate reliably under permission, auditing, and rollback mechanisms.
Together, these capabilities function like a universal chassis for industrial AI, addressing the foundational engineering problems that every developer must otherwise solve from scratch.
With this foundation in place, ecosystem partners do not need to keep reinventing the wheel.
Developers in the automotive, semiconductor, energy, and food and beverage industries can build on validated capabilities, add their own industry know-how, and develop agents for different equipment, processes, and business workflows.
The same industrial foundation can therefore give rise to hundreds of applications for different scenarios.
These applications can then use the Siemens Xcelerator Marketplace as an entry point to reach more customers.
Each successful deployment means the next one no longer has to start from zero.
The more partners that connect to the platform and the more scenarios it covers, the richer its accumulated industrial knowledge becomes—and the easier it is to develop and deploy new agents.
This is the fundamental difference between Siemens Xcelerator and an ordinary software marketplace.
A marketplace is designed to “sell products.” Siemens Xcelerator is designed to “help products continuously evolve in real-world industrial settings.”
Manufacturing companies are also voting for this approach through their choices.
The Report shows that 68% of companies are willing to share data and integrate technology with external technology providers.
Siemens is taking this a step further by opening up Chinese factory scenarios and attracting developers to the industrial AI field through Siemens Xcelerator competitions, turning real-world requirements directly into a test bed for ecosystem innovation.
In this sense, Siemens Xcelerator replicates a methodology of “validation, accumulation, development, distribution, and revalidation.”
When an agent can write PLC code, that is point intelligence.
But only when different partners can build their own agents for different processes, accumulate their own know-how, generate their own ROI, and then bring one successful implementation to more factories does the large-scale adoption of industrial AI truly begin.
Ultimately, the spread of office AI depends on giving everyone access to a dialogue box.
The spread of industrial AI, by contrast, depends on turning capabilities validated in one factory into productivity that can be safely called upon by many more factories.
That is what Siemens Xcelerator aims to advance.
When agents are no longer star projects confined to showcase factories, but become foundational capabilities that engineers can use at any time—like PLCs and industrial software—industrial AI will finally have moved beyond the exhibition stage and truly entered the production line.