{"id":6730,"date":"2025-09-09T19:11:18","date_gmt":"2025-09-10T02:11:18","guid":{"rendered":"https:\/\/staging.xlmsolutions.com\/blog\/developing-ai-agents-for-plm-a-glimpse-into-the-mcp-model\/"},"modified":"2025-09-09T19:11:18","modified_gmt":"2025-09-10T02:11:18","slug":"developing-ai-agents-for-plm-a-glimpse-into-the-mcp-model","status":"publish","type":"post","link":"https:\/\/staging.xlmsolutions.com\/es\/blog\/developing-ai-agents-for-plm-a-glimpse-into-the-mcp-model\/","title":{"rendered":"Developing AI Agents for PLM: A Glimpse into the MCP Model"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">As artificial intelligence continues to evolve, its role in enterprise engineering environments \u2013 especially PLM \u2013 is becoming more compelling. At xLM Solutions, we\u2019ve begun exploring how AI agents can transform the way users interact with Product Lifecycle Management systems by automating workflows, retrieving data, and making smart, contextual decisions on behalf of users.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In a <\/span><a href=\"https:\/\/youtu.be\/_dqJGJqIwy8\"><span style=\"font-weight: 400;\">recent video<\/span><\/a><span style=\"font-weight: 400;\">, I showcased how to connect commercial AI large language models (LLM) and chat tools like OpenAI\u2019s ChatGPT and Claude to PLM systems such as<\/span><b> 3D<\/b><span style=\"font-weight: 400;\">EXPERIENCE and SOLIDWORKS PDM. But instead of relying on traditional, brittle API integrations, we used a new approach: Model-Client-Proxy (MCP) \u2013 a standardized framework that simplifies how AI agents interface with external systems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Keep reading for a high-level overview of the concepts behind AI agents and the MCP model, and what we demonstrated in the <\/span><a href=\"https:\/\/youtu.be\/_dqJGJqIwy8\"><span style=\"font-weight: 400;\">video<\/span><\/a><span style=\"font-weight: 400;\">. A deeper dive, including configuration details and code samples, will be offered in our upcoming webinar.<\/span><\/p>\n<h4><b>What Is an AI Agent?<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">An AI agent is more than a chatbot. It\u2019s an autonomous system that can take actions on a user\u2019s behalf, learn from interactions, adapt to different contexts, and intelligently integrate with external systems. In the PLM world, that means helping engineers and decision-makers:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Retrieve part information or BOMs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identify discrepancies across systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Support risk and compliance analysis<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Improve supply chain responsiveness<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Unlike traditional automation, which runs predefined scripts, AI agents operate more dynamically. When connected to machine learning models (we focused on LLM in our demo), they can even improve over time.<\/span><\/p>\n<h4><b>The Problem with Traditional Integrations<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Historically, integrating AI models with PLM systems required custom code for each connection \u2013 writing and maintaining direct API calls for platforms like SOLIDWORKS PDM, <\/span><b>3D<\/b><span style=\"font-weight: 400;\">EXPERIENCE, Aras, OpenBOM. Autodesk Vault, Arena (these are just some of the systems we support). This approach has some serious drawbacks:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">High maintenance overhead when APIs change<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Security challenges across multiple interfaces<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Scalability issues as systems and use cases expand<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In short, traditional integration methods are difficult to manage and not sustainable as AI capabilities grow.<\/span><\/p>\n<h4><b>Introducing the MCP Model<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">The Model-Client-Proxy (MCP) framework offers a more scalable, secure, and standardized alternative. Instead of custom-building every integration, MCP acts as a universal adapter \u2013 think of it like a USB-C port for software systems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With MCP:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The AI agent connects to a single server<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Requests and responses are handled through a shared protocol<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A discovery and reflection process exposes available MCP server tools to the model<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Contextual prompts allow the agent to intelligently choose which tool to invoke<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This allows one integration point for all external systems \u2013 whether it\u2019s PLM, ERP, MES, or vendor management \u2013 making the setup far more future-proof and manageable.<\/span><\/p>\n<h4><b>Claude, ChatGPT, and a Custom Console<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">In the <\/span><a href=\"https:\/\/youtu.be\/_dqJGJqIwy8\"><span style=\"font-weight: 400;\">video<\/span><\/a><span style=\"font-weight: 400;\">, I showcased three different environments where the MCP model was used to connect AI tools to PLM systems:<\/span><\/p>\n<ol>\n<li><b> Claude AI Desktop Application<\/b><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Claude was configured to reflect available MCP tools, such as \u201cplatform3dx\u201d and \u201cget platform SOLIDWORKS PDM.\u201d When asked for part data, it successfully retrieved information from both systems in real time \u2013 without any direct coding or system-specific logic embedded in the prompt.<\/span><\/p>\n<ol start=\"2\">\n<li><b> Custom Console Application<\/b><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">I also built a simple console tool to show that you don\u2019t need a commercial chatbot. Using Claude in the backend, the same MCP tools were invoked, proving that you can create lightweight, custom applications that leverage AI agent functionality.<\/span><\/p>\n<ol start=\"3\">\n<li><b> ChatGPT Web Interface<\/b><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Using a third-party browser extension and MCP tools registered with the model, I showed how ChatGPT could retrieve and even visualize data pulled from external PLM systems. It identified the correct MCP tools on its own and executed the retrieval as if it were a human assistant.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In each scenario, the AI agent was able to seamlessly interact with SOLIDWORKS PDM and <\/span><b>3D<\/b><span style=\"font-weight: 400;\">EXPERIENCE using a consistent and secure interface.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2e3b<\/span><\/p>\n<h4><b>What\u2019s Next: Behind the Scenes and Model Building<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">The goal of this session was to offer a sneak peek at what\u2019s possible. In our upcoming webinar, we\u2019ll go deeper into:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The backend configuration of the MCP client and server<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Code examples to help you build your own AI agent<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Best practices for managing prompts, tool registration, and user permissions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A high-level introduction to building or fine-tuning AI models, including:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Data collection and preprocessing<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Training strategies<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Deployment considerations<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hosting your model securely (vs. using public LLM chatbots like ChatGPT, Claude, etc.)<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This is just the beginning of what\u2019s possible with AI agents in PLM \u2013 and at xLM Solutions, we\u2019re actively working with clients to explore and implement these technologies in real-world environments.<\/span><\/p>\n<h4><b>Looking Ahead<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Stay tuned for our upcoming webinar, where we\u2019ll walk through the technical architecture and implementation steps in more detail.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the meantime, <\/span><a href=\"https:\/\/youtu.be\/_dqJGJqIwy8\"><span style=\"font-weight: 400;\">watch the video<\/span><\/a><span style=\"font-weight: 400;\">, subscribe to our newsletter, follow xLM Solutions on <\/span><a href=\"https:\/\/www.linkedin.com\/company\/xlm-solutions\/\"><span style=\"font-weight: 400;\">LinkedIn<\/span><\/a><span style=\"font-weight: 400;\">, or <\/span><a href=\"https:\/\/staging.xlmsolutions.com\/es\/contacto\/\"><span style=\"font-weight: 400;\">reach out to our team<\/span><\/a><span style=\"font-weight: 400;\"> if you\u2019re ready to explore AI-powered PLM solutions for your organization.<\/span><\/p>\n<p>&nbsp;<\/p>\n<div class=\"pld-like-dislike-wrap pld-template-2\">\r\n    <div class=\"pld-like-wrap  pld-common-wrap\">\r\n    <a href=\"javascript:void(0)\" class=\"pld-like-trigger pld-like-dislike-trigger  \" title=\"Like\" data-post-id=\"6730\" data-trigger-type=\"like\" data-restriction=\"no\" data-already-liked=\"0\">\r\n                        <i class=\"fas fa-heart\"><\/i>\r\n                <\/a>\r\n    <span class=\"pld-like-count-wrap pld-count-wrap\">    <\/span>\r\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>As artificial intelligence continues to evolve, its role in enterprise engineering environments \u2013 especially PLM \u2013 is becoming more compelling. At xLM Solutions, we\u2019ve begun exploring how AI agents can [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":6728,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","footnotes":"","_wp_rev_ctl_limit":""},"categories":[300],"tags":[],"class_list":["post-6730","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-plm"],"acf":[],"featured_image_src":{"landsacpe":["https:\/\/staging.xlmsolutions.com\/wp-content\/uploads\/2025\/09\/AIBlogxLM.png",850,445,false],"list":["https:\/\/staging.xlmsolutions.com\/wp-content\/uploads\/2025\/09\/AIBlogxLM.png",463,242,false],"medium":["https:\/\/staging.xlmsolutions.com\/wp-content\/uploads\/2025\/09\/AIBlogxLM-300x157.png",300,157,true],"full":["https:\/\/staging.xlmsolutions.com\/wp-content\/uploads\/2025\/09\/AIBlogxLM.png",1198,627,false]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Developing AI Agents for PLM: A Glimpse into the MCP Model - XLM Solutions<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Developing AI Agents for PLM: A Glimpse into the MCP Model\" \/>\n<meta property=\"og:description\" content=\"As artificial intelligence continues to evolve, its role in enterprise engineering environments \u2013 especially PLM \u2013 is becoming more compelling. 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