{"id":545357,"date":"2026-09-08T11:32:01","date_gmt":"2026-09-08T09:32:01","guid":{"rendered":"https:\/\/silicon-saxony.de\/fraunhofer-iwu-agentic-ai-the-next-big-thing-after-image-recognition-and-generative-ai-in-industry-as-well\/"},"modified":"2026-09-08T11:32:01","modified_gmt":"2026-09-08T09:32:01","slug":"fraunhofer-iwu-agentic-ai-the-next-big-thing-after-image-recognition-and-generative-ai-in-industry-as-well","status":"publish","type":"post","link":"https:\/\/silicon-saxony.de\/en\/fraunhofer-iwu-agentic-ai-the-next-big-thing-after-image-recognition-and-generative-ai-in-industry-as-well\/","title":{"rendered":"Fraunhofer IWU: Agentic AI \u2013 &#8220;The Next Big Thing&#8221; after image recognition and generative AI \u2013 in industry as well"},"content":{"rendered":"<p><img decoding=\"async\" style=\"width: 25%;\" src=\"https:\/\/cdn.pblzr.de\/dacbb27c-1270-4041-b681-e2b95f06f8a1\/2026\/09\/fraunhoferiwu-logo-400x300_TEXT.jpg\"><\/p>\n<p>Chatbots like ChatGPT are already marking the transition to agent-based AI in everyday life: Users see that their query is broken down into several steps (searching, evaluating, summarizing), but they do not see the logic the system uses to select and sequence the appropriate tools. Agent-based AI is already demonstrating its strengths in various applications across software development, customer service, IT operations, knowledge management, and administrative business processes\u2014anywhere complex tasks consist of multiple steps and must be coordinated across different systems.&nbsp;<\/p>\n<ul>\n<li>When used in customer service, agent-based AI can independently handle large portions of customer inquiries, not only answering questions but also processing entire service workflows and making autonomous decisions within defined rules.<\/li>\n<li>In software development, modern coding agents analyze requirements, write code, run tests, and (iteratively) fix bugs.<\/li>\n<li>In retail, well-orchestrated AI agents can assist with inventory management, demand forecasting, logistics, and product management. The agents are designed to autonomously combine information from various systems, prepare operational decisions, and, in some cases, execute them.<\/li>\n<\/ul>\n<h3 class=\"\">Orcha-3: Transfer to Industrial Production<\/h3>\n<p>The Orcha-3 platform, developed at Fraunhofer IWU, applies this approach to production engineering. Orcha-3 is designed for problems that have been difficult to address with individual AI models to date: open-ended questions along a process chain, where the answer does not result from a single measurement but from many interdependent steps, and where the next logical step is not determined until the previous one has been evaluated.<\/p>\n<p>An example: In a process chain consisting of forming and joining operations, dimensional deviations occur at the end, and the cause remains unknown. Orcha-3 automatically breaks this problem down into individual steps \u2014retrieving process data from the machine and plant environment, merging time series from multiple stations, calculating correlations, simulating parameter variations, evaluating interim results\u2014and decides anew after each step how to proceed. Each of these steps is executed by a specialized agent with a clearly defined task.<\/p>\n<p>Crucial for its use in manufacturing is the division of labor between the language model and execution: The language model plans, but does not itself access any production system. Database queries, calculations, and simulations run exclusively locally within the agents. Raw data does not leave the corporate network; the model receives only a curated, fully logged view of the planning status. This view is generated and stored deterministically for each planning step. This makes it possible to verify retrospectively which information was actually sent to the model. Process know-how embedded in manufacturing parameters is often a company\u2019s true competitive advantage; Orcha-3 is designed so that the use of powerful language models does not require the disclosure of this knowledge.<\/p>\n<p>For the same reason, the entire execution is deterministically orchestrated (rule-based execution, with every decision reconstructible after the fact) and seamlessly logged: Every step, every intermediate result, and every decision made by the platform is stored and remains traceable even months later. For a recommendation that enters series production, this traceability is not a convenience feature, but a prerequisite. In addition, Orcha-3 can execute missing evaluation steps in the form of small programs even when the existing toolkit is insufficient for a given problem.<\/p>\n<p>The platform is currently designed as a research and testing environment. The medium-term goal is to expand from individual process chains to the production system as a whole, addressing issues such as machine and personnel deployment, space requirements, and logistics concepts\u2014questions that today can only be answered through time-consuming manual analyses.<\/p>\n<h3 class=\"\">Synthetic data for modeling a wide range of tasks and use cases<\/h3>\n<p>Planning or optimizing a complex production process requires a solid data foundation. The initial chicken-and-egg dilemma can be elegantly solved\u2014using specially generated synthesized data based on virtual process chains that replicate real-world processes, such as individual forming steps. AI can now build upon this virtual process chain to identify opportunities for improvement. This makes the problem and task definitions virtually infinitely variable.<\/p>\n<h3 class=\"\">Complexity that can only be managed with agent-based AI<\/h3>\n<p>In multi-stage manufacturing processes, there are questions that should be clarified but usually remain unanswered because resolving them would take days of data processing:<\/p>\n<p>\u201cAt the end of the process chain, parts are out of tolerance, yet every single station reports \u2018OK\u2019.\u201d<br \/>\u201cWhere does the deviation come from?\u201d<br \/>\u201cThe material supplier has changed; the data sheet looks the same, but the process is no longer running as it did before. Which parameters need to be adjusted?\u201d<br \/>\u201cThe scrap rate fluctuates between shifts. There\u2019s a hunch, but no one to spend two weeks cross-checking process data against it.\u201d<\/p>\n<p>Questions like these can\u2019t be answered with a single analysis, but with many: consolidating data from multiple stations, examining correlations, rejecting one hypothesis, and formulating the next. Orcha-3 handles exactly this process autonomously, making every step traceable.<\/p>\n<p>The platform is fully functional and has been tested on virtual process chains. The next step is to apply it to real-world manufacturing challenges; to that end, we are seeking to engage with companies that are currently facing such a specific challenge. Together, we\u2019ll assess whether this specific task can be addressed using the Orcha-3 approach, what the available data allows for, and what a first step might look like.<\/p>\n<h3 class=\"\">Agentic AI \/ Agentic AI<\/h3>\n<p>Agentic AI refers to AI systems that act as autonomous agents to independently perform complex, multi-step tasks. Unlike conventional chatbots, which only respond to direct commands, these systems can process complex tasks and independently utilize tools (other agents or software) as needed. They break down larger tasks into several logical sections, do not rely on frequent human intervention, and respond flexibly to short-term or unforeseen changes.&nbsp;<\/p>\n<h3 class=\"\">The Orcha-3 Platform<\/h3>\n<p>Orcha-3 processes complex tasks as so-called \u201cjobs\u201d and handles them in a structured manner across multiple loops. At its core, the platform is based on a clearly separated architecture consisting of an API (application programming interface), an orchestrator, a supervisor, and agents. Consistently state-based: All information is persistently stored in the file system, ensuring complete transparency, traceability, and recoverability. The job is processed in a recurring cycle of planning and execution:<\/p>\n<ul>\n<li>A Planner agent analyzes the current state and decides on the next steps.<\/li>\n<li>This results in tasks, each of which is executed by specialized agents.<\/li>\n<li>Results are stored and integrated into the next planning cycle.<\/li>\n<li>The process repeats until a solution is reached or a defined termination condition is met.<\/li>\n<\/ul>\n<p>&#8211; &#8211; &#8211; &#8211; &#8211;<\/p>\n<h4 class=\"\">Related Links<\/h4>\n<p><a href=\"http:\/\/www.iwu.fraunhofer.de\" target=\"_blank\">\ud83d\udc49 www.iwu.fraunhofer.de<\/a>&nbsp; &nbsp;<\/p>\n<p><i>Photo: Fraunhofer IWU<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>September 8, 2026. The use of image data comparison marked the beginning of artificial intelligence\u2019s entry into production several years ago. Since then, neural networks have been trained\u2014not only, but primarily for quality control purposes\u2014to detect deviations (from the target) by comparing a component (actual) with reference images. Meanwhile, generative AI based on large language models assists in the step-by-step resolution of typically narrowly defined problems. In an iterative process, humans provide prompts until a solution is reached. Agentic AI works quite differently: It selects the AI tools (agents) required for highly complex tasks and develops solutions largely on its own.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[4764],"tags":[4759,2043],"class_list":["post-545357","post","type-post","status-publish","format-standard","hentry","category-software-en","tag-artificial-intelligence-ai","tag-digitalization"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Fraunhofer IWU: Agentic AI \u2013 &quot;The Next Big Thing&quot; after image recognition and generative AI \u2013 in industry as well - Silicon Saxony<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/silicon-saxony.de\/en\/fraunhofer-iwu-agentic-ai-the-next-big-thing-after-image-recognition-and-generative-ai-in-industry-as-well\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fraunhofer IWU: Agentic AI \u2013 &quot;The Next Big Thing&quot; after image recognition and generative AI \u2013 in industry as well - Silicon Saxony\" \/>\n<meta property=\"og:description\" content=\"September 8, 2026. The use of image data comparison marked the beginning of artificial intelligence\u2019s entry into production several years ago. Since then, neural networks have been trained\u2014not only, but primarily for quality control purposes\u2014to detect deviations (from the target) by comparing a component (actual) with reference images. Meanwhile, generative AI based on large language models assists in the step-by-step resolution of typically narrowly defined problems. In an iterative process, humans provide prompts until a solution is reached. 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