{"id":2273,"date":"2025-01-22T16:48:12","date_gmt":"2025-01-23T00:48:12","guid":{"rendered":"https:\/\/superwomansolutions.com\/engtechnica\/?p=2273"},"modified":"2025-01-23T16:54:26","modified_gmt":"2025-01-24T00:54:26","slug":"ai-comes-to-design-as-agents","status":"publish","type":"post","link":"https:\/\/superwomansolutions.com\/engtechnica\/ai-comes-to-design-as-agents\/","title":{"rendered":"Agentic Engineering: How AI Automata Will Participate in Engineering in 2025"},"content":{"rendered":"\n

This post was initially published on Blake Courter<\/a>. It is reprinted here by permission.<\/em><\/p>\n\n\n\n

\"\"
The crew of the Enterprise collaborates with The Computer to reconstruct a medical table in the Holodeck. Although this episode of TNG jumped the shark, this scene influenced me greatly.<\/em><\/figcaption><\/figure>\n\n\n\n

By Blake Courter, founder of Gradient Control Laboratories<\/strong><\/a>, with input from Luke Church of GCL and Brad Rothenberg of nTop. It includes significant contributions and feedback from: Mark Burhop of Sciath aiM; Jacomo Corbo of PhysicsX; Kiegan Lenihan of xNilio; Peter Harman of Infinitive; Saigopal Nelaturi of C-Infinity; Hugo Nordell of Encube; Alex Huckstepp of Uptool; Neel Goldy Kumar of Intact Solutions; Blake Reeves of Pasteur Labs; Andy Fine of Fine Physics; Kyle Bernhardt of Collectiv and Claude 3.5 Sonnet.<\/em><\/p>\n\n\n\n

The future of AI in engineering won\u2019t arrive as a single super intelligent design system. Instead, 2025 will see the rise of specialized AI agents that work alongside engineers throughout the product lifecycle \u2014 simulating assemblies, automating documentation, optimizing components and configuring supply chains. These agents, operating both within existing tools and through new platforms, represent a fundamental shift in how we develop products, one that promises to accelerate and enhance the engineering process dramatically. Success will require solving key technical challenges around security and agent coordination<\/p>\n\n\n\n

Vision<\/strong><\/p>\n\n\n\n

The engineering industry\u2019s vision for AI-powered design tools seems to mirror science fiction, from Star Trek\u2019s Holodeck to Tony Stark\u2019s workshop. The narrative follows three steps:<\/p>\n\n\n\n

    \n
  1. An engineer declares intent, describing a design objective, its constraints and performance goals;<\/li>\n\n\n\n
  2. The computer synthesizes a complete design proposal, from geometry to materials to manufacturing;<\/li>\n\n\n\n
  3. Through rapid iteration and feedback, the engineer and AI converge on an optimal solution.<\/li>\n<\/ol>\n\n\n\n

    The vision of computing from Star Trek parallels what we\u2019re seeing with AI art tools like Midjourney and Stable Diffusion: creative systems driven by geometric suggestions, natural language prompts and iterative refinement. Could engineering tools evolve similarly, adding physics, manufacturing constraints and performance optimization to the mix?<\/p>\n\n\n\n

    The path to these future builds on three generations of generative engineering software:<\/p>\n\n\n\n

      \n
    1. Procedural Generation: While early automation such as AutoLISP<\/strong><\/a> and Pro\/ENGINEER<\/strong><\/a> elevated geometry languages from low-level modeling instructions to occasionally capturing true design intent, such business logic requires extensive programming or additional product layers like expert systems or configurators.<\/li>\n\n\n\n
    2. Topology Optimization: These tools synthesize geometry directly from engineering requirements and boundary conditions. While powerful for specific use cases like lightweighting parts, current systems require significant human effort to produce manufacture-ready designs.<\/li>\n\n\n\n
    3. Machine Learning: Emerging ML approaches have shown promise in architectural applications like space planning and restoration. However, they\u2019ve yet to achieve comparable success in mechanical engineering, where physical constraints and manufacturing requirements pose greater challenges.<\/li>\n<\/ol>\n\n\n\n

      Each of these approaches will evolve into more sophisticated agents that proactively participate in the design process as well as interfaces supporting such agents. But to understand how this transformation will actually unfold, we need to look beyond the Hollywood vision to the day-to-day realities of engineering work.<\/p>\n\n\n\n

      Reality<\/strong><\/p>\n\n\n\n

      I entered this industry inspired by the sci-fi generative vision but quickly discovered a different reality: engineering isn\u2019t primarily about raw ideation. The mechanical industry is dominated by reuse, the architecture and construction industries by codes, standards and communities of practice<\/strong><\/a>. Synthesis seems seductive, but must conform to existing designs and established practices. Although the engineering industry has achieved significant standardization, for standards bodies and regulatory frameworks, AI may run afoul of copyright and licensing constraints.<\/p>\n\n\n\n

      The data challenge runs deeper than just access. High-quality engineering data remains locked in proprietary PLM and BIM systems and even when available, frequently fails to capture actual design intent. While the abundant public images that feed Midjourney and Stable Diffusion are relatively self-contained, engineering datasets remain siloed in a Babel of proprietary, binary file streams. More fundamentally, while evaluating an AI-generated image is nearly instantaneous, validating an engineering design can be more expensive than creating it correct by construction<\/strong> in the first place. This explains why engineering embraces standards not just because of tradition but as a practical necessity for managing complexity and cost.<\/p>\n\n\n\n

      Yet at GCL, we\u2019re seeing an encouraging trend: a constellation of startups are finding ways to extract value from this real-world data, becoming useful collaborators in the product development process. While these AI tools currently run independently from engineering databases, 2025 will mark a turning point as they begin operating autonomously within CAD and PLM systems.<\/p>\n\n\n\n

      The major wave of AI in engineering won\u2019t come from a single massive system that realizes the sci-fi vision. Instead, it will emerge through many specialized agents, each adding value to and perhaps transforming, existing systems and workflows.<\/p>\n\n\n\n

      Applications<\/strong><\/p>\n\n\n\n

      If engineering\u2019s AI future lies in specialized agents rather than monolithic systems, how will this transformation unfold? Let\u2019s examine the emerging applications in order of technology readiness, starting with one of the oldest tools in engineering: surrogate modeling.<\/p>\n\n\n\n

      Surrogate Modeling<\/strong><\/p>\n\n\n\n

      At its core, surrogate modeling resembles sophisticated curve fitting: something as common as interpolating thermodynamics tables or a floating-point trig computation. But modern surrogate models do more than just fit complex mathematical functions to high-dimensional data. In fields like astrophysics, surrogate models aim to reproduce the underlying physical processes themselves, not just their outputs. This contrasts with most AI training, which typically focuses on replicating outcomes without modeling the generative process. When today\u2019s AI experts talk about \u201ctraining a model,\u201d they\u2019re often building outcome-focused approximations at unprecedented scale.<\/p>\n\n\n\n

      Surrogate modeling supplants slow or iterative processes with instantaneous answers. Companies like PhysicsX<\/strong><\/a>, Pasteur Labs<\/strong><\/a> and Neural Concept<\/strong><\/a> are applying this approach to accelerate physics simulations and predict experimental outcomes, while multi-disciplinary optimization tools like Noesis Optimus<\/strong><\/a>, Siemens PLM HEEDS<\/strong><\/a>, ANSYS ModelCenter<\/strong><\/a> and Esteco ModeFrontier<\/strong><\/a> integrate surrogate models into broader system optimization workflows. As the surrogate models become increasingly comprehensive, complex workflows can be led with models.<\/p>\n\n\n\n

      For example, the aircraft design process I learned in school starts with payload, speed and range requirements that drive initial wing sizing calculations. Airfoil choice leads to drag and reaction forces, which inform iterative cycles of aerodynamic and structural analysis, progressively refining the design while balancing performance metrics against practical constraints until reaching an optimal solution. We were taught techniques for shortening the inevitable iteration loops.<\/p>\n\n\n\n

      PhysicsX\u2019s ai.rplane<\/strong><\/a> demonstrator obviates such iteration. The model subjects a set of plane-like geometries to virtual wind tunnel tests and captures performance as well as sizing metrics, plotting the results on some flattened latent space. Perhaps, in the near future of systems engineering, identifying iteration loops becomes a Kaizen<\/a> event for surrogates?<\/p>\n\n\n\n

      \"\"
      In this screenshot from ai.rplane<\/strong><\/a>, I\u2019ve sampled some points in what appears to be a 3D projection of latent aircraft space, with a bird of prey selected. The red region shows airframes too small for my payload\u2019s volume. I overlaid three thumbnails above the samples at the top to show how the latent space appears to interpolate the shape space, with the slenderest too cramped for the desired payload.<\/em><\/figcaption><\/figure>\n\n\n\n

      The evolution to agents takes this further: surrogate models become active participants in the engineering process. While these predictions still require validation for critical decisions, they enable continuous exploration of complex design spaces that would be impractical to simulate directly. The same approach applies to supply chain optimization, where surrogates can model the interactions between cost, availability and performance across thousands of components.<\/p>\n\n\n\n

      Quoting, Costing and Time Estimation<\/strong><\/p>\n\n\n\n

      Over the past dozen years, I\u2019ve had a close look at about a dozen cost and time estimators for CAM, 3D printing and production processes. Manufacturing marketplaces like Xometry<\/strong><\/a>, Protolabs<\/strong><\/a>, Meviy<\/strong><\/a> and Fictiv<\/strong><\/a> compete on some Pareto curve of price, turnaround and time-to-quote; they all have accumulated valuable training data from millions of parts.<\/p>\n\n\n\n

      Neural-net based estimators aren\u2019t new: they\u2019ve shipped on manufacturing hardware for at least two decades, with 3D printer vendors using them to predict print times and material consumption. MES systems have extended this approach to more complex manufacturing operations, learning from actual production data. Keep an eye on startups like Encube<\/strong><\/a> and Uptool<\/strong><\/a> to deepen AI and ML\u2019s role in manufacturing unit economics and arbitrage.<\/p>\n\n\n\n

      AI agents can \u201cleft-shift\u201d cost estimation: moving it earlier in the design process, directly into CAD and PLM systems. Users could receive continuous cost updates as they design, similar to how simulation feedback appears at the end of their feature tree. While some CAD add-ins already provide real-time quoting, these capabilities remain rare differentiators. Standardized protocols for cost estimation agents would make such services ubiquitous, benefiting both customers and vendors.<\/p>\n\n\n\n

      <\/span>\"\"
      \n

      <\/p>\n<\/div><\/div>\n\n\n\n

      Misumi Meviy\u2019s CNC quoting tool automatically recognizes manufacturing features and shows you the manufacturing plan. I expect they will be able to handle this slitting feature someday, as well as the hole before it.<\/em><\/p>\n\n\n\n

      What about more specialized machine shops? Uptool is building automation to interpret quotes, including nuances like tight tolerances that are not handled by the commodity vendors, all while keeping experts in the loop.<\/p>\n\n\n\n

      A reasonable machinist might also recommend a design change. The manufacturing industry has long lamented designers\u2019 apathy of design-for-manufacturing. Like a good machinist, Encube suggests small design changes that lower manufacturing costs. Could an approach like Encube\u2019s finally left-shift DFM into CAD?<\/p>\n\n\n\n

      Drafting<\/strong><\/p>\n\n\n\n

      Sometimes, a CAD part isn\u2019t enough to produce a quote, but a drawing usually is. I\u2019ve recently been helping engineer a family of fancy brackets for some friends\u2019 design project that called for interlocking tapered pockets, a geometry case that broke the quoting tools I mentioned above. The local CNC shop was happy to take the work, but needed drawings. I tried to get away with only detailing the most complicated one and hoped they could take it from there. But no, they needed a drawing for every one. Then, after sending all the drawings, they asked me for tolerances, which meant I had to mostly redo the first, most complicated drawing because most of my dimensions had disappeared. Deducing the tolerances became a gut-wrenching process not so much for the math anxiety, but because I feared their sarcasm for a tenth of a thousandth of an inch on those tapers. Somebody with human intelligence probably saw that coming and sent me a follow-up not to bother with the tolerances after all.<\/p>\n\n\n\n

      This story illustrates three universal truths about engineering drawings: everybody wants them to be complete, accurate and up to date; nobody wants to make them; and they often contain low-confidence information dressed up as precise specifications.<\/p>\n\n\n\n

      \"\"
      Apparently, a human-drawn drawing, via Spencer Wright et al.\u2019s excellent newsletter, Scope of Work. IMO, it could use some whitespace.<\/em><\/figcaption><\/figure>\n\n\n\n

      Enter generative AI drawings. Companies like Hanomi<\/strong><\/a>, Drafter<\/strong><\/a> and Swapp<\/strong><\/a> are showing impressive results that go beyond automated dimensioning; they\u2019re building foundation models for geometric dimensioning and tolerancing (GD&T), by necessity solving sophisticated generative problems to deliver drawing synthesis. These systems will likely expand to generate geometry, especially basic parts like plates, brackets and other common components.<\/p>\n\n\n\n

      Packaged as agents, these capabilities will integrate directly into our CAD workflows: finished drawings appear automatically at the bottom of our feature trees, complete with appropriate GD&T back-propagated to the 3D model. The agents could even suggest part variants optimized for different manufacturing processes, each with associated quotes and lead times.<\/p>\n\n\n\n

      Mark Burhop<\/strong><\/a> anticipates that similar downstream agents will left-shift much more, expanding from manufacturing agents that create interchangeable parts across manufacturing processes or vendors to predictive serviceability, repair, wear, sustainability and circularity.<\/p>\n\n\n\n

      Supplier Ggents<\/strong><\/p>\n\n\n\n

      In PLM and BIM, most designs are assembled from standard components, such as linear rails, aluminum extrusions and motors in machine design and studs, windows and curtain wall systems in AEC. For commodity items like fasteners and materials, CAD systems may provide integrated databases of so-called smart components, suggesting possible interaction paradigms. However, this approach misses nuance in more competitive technical components, which is why suppliers often attribute their success to high-acumen sales engineers.<\/p>\n\n\n\n

      Some of the highest-growth engineering software startups, like Vention<\/strong><\/a> and Higharc<\/strong><\/a>, recognize this reality: they\u2019ve built custom CAD systems where the software becomes part of the overall product experience. Instead of requiring their suppliers to purchase and learn software up-front, the future end-users of the actual machines or homeowners can immediately start configuring solutions.<\/p>\n\n\n\n

      For mainstream engineers, designing a machine or building remains an odyssey through drawings, CAD files and sales literature. LLMs and generative 3D technology promise to transform this experience by transforming disparate product data into agentic actors in CAD, PLM and BIM systems.<\/p>\n\n\n\n

      Consider designing a custom manufacturing device that needs an XY table, common in 3D printers and CNC machines. You want to optimize between rigidity and speed across different rail configurations and drive systems. Companies like Misumi<\/strong><\/a> offer sophisticated configurators, but you still need to make detailed decisions about pre-loading and seal wear just to get a STEP file that requires manual assembly. And that\u2019s just the first rail.<\/p>\n\n\n\n

      Imagine instead: you draw a box in your CAD assembly, specify your end effector load and let supplier agents compete for your business. You might see variations from Misumi, THK<\/strong><\/a> and HIWIN<\/strong><\/a>, along with unexpected proposals from vendors you hadn\u2019t considered. LLMs handle your questions like experienced sales engineers, reconfiguring designs on-the-fly, complete with lead times and volume pricing.<\/p>\n\n\n\n

      These systems, funded by suppliers\u2019 sales and marketing teams, raise interesting security questions: How do we enable agents to make decisions based on CAD data without risking intellectual property? Could suppliers optimize production by analyzing where their components appear in designs? Will trusted third parties be needed?<\/p>\n\n\n\n

      <\/span>\"\"
      \n

      <\/p>\n<\/div><\/div>\n\n\n\n

      Meshtastic Designer, an IoT design tool powered by Infinitive, a configurator platform that generates designs from engineering requirements while also informing e-commerce and ERP.<\/em><\/p>\n\n\n\n

      Infinitive<\/strong><\/a> appears to be pioneering this approach in discrete manufacturing, while several startups explore AI-powered AEC marketplaces. This model of embedding design software into revenue streams is gaining traction among engineering software startups.<\/p>\n\n\n\n

      Simulation<\/strong><\/p>\n\n\n\n

      When SpaceClaim<\/strong><\/a> started to take off with simulation users, I had the opportunity to problem-solve with all sorts of engineers on a daily basis. Given that all models are wrong<\/strong><\/a>, I\u2019d ask questions like \u201chow do you know that your results are accurate\u201d and \u201chow do you build confidence that you\u2019ve handled all the cases,\u201d to which I\u2019d hear answers like \u201cmy results aren\u2019t accurate, but my derivatives have the right sign\u201d and \u201cthere\u2019s no way we have enough time to handle all the cases we can think of.\u201d And some everyday engineering validation requirements like drop testing and assembly-level resonance testing seem like simulation fantasy.<\/p>\n\n\n\n

      We already discussed surrogate models, which can build confidence when connecting simulation results to empirical data. Using various forms of neural networks and similar approaches, companies like PhysicsX<\/strong><\/a>, Pasteur Labs<\/strong><\/a> and Neural Concept<\/strong><\/a> may accelerate results by two to six orders of magnitude. These vendors are developing and releasing increasingly advanced and differentiated models, much like how OpenAI and Claude.ai are competing on LLMs.<\/p>\n\n\n\n

      The next step is embedding this capability directly in the design process. Prominently in AEC, Autodesk has implemented surrogate models to deliver near-real time CFD visualization in their Forma<\/strong><\/a> product. Simulation agents will continuously analyze CAD assemblies, working from the PLM context to evaluate everything in sight. Interfaces and kinematics will emerge automatically from geometric placement; quick modal studies will detect gaps and interferences like a real-time spell-checker. Combined with increased cloud and GPU resources, we can finally start to cover all the simulation edge cases. Perhaps we can even measure the completeness of our simulated test coverage similarly to how the software industry measures code coverage in QA.<\/p>\n\n\n\n

      These agents will provide feedback similar to how modern programming environments guide developers: immediate, context-aware and actionable. xNilio (in stealth mode; material used with permission) demonstrates this with their product-level agent system, while companies like Intact Solutions<\/strong><\/a> and CoreForm<\/strong><\/a> show how meshless simulation can directly use adjacent parts in assemblies as boundary conditions. The SimSolid technology at Altair<\/strong><\/a> already excels at these assembly-level problems.<\/p>\n\n\n\n

      \"\"
       xNilio identifies patterns and simulates product-level problems, functioning as a continuous, offline design review.<\/em><\/figcaption><\/figure>\n\n\n\n

      Large OEMs often process enormous volumes of product revisions per year, creating an overwhelming flow of changes that must be verified for each product and configuration. Each change has the potential to disrupt assembly instructions, impact fitment, or require revalidation for multiple product options. Engineers face the difficult task of testing every configuration, which is extremely slow without automation. C-infinity<\/strong><\/a> and Dirac<\/strong><\/a> are producing approaches to navigate and validate the configuration space of complex product assembly.<\/p>\n\n\n\n

      The challenge isn\u2019t just technical but interactional: how do we present all this intelligence without overwhelming engineers? Simple notifications, as I learned from the early overuse of Slack integrations, can quickly become noise rather than signal. Similarly, in software development environments, it took about two decades for static analysis to evolve into useful autocomplete. The future might lie in more subtle interfaces: annotations in digital mockups, contextual reports, or selective alerts based on risk levels. But we\u2019ll need to carefully balance comprehensive analysis against the practical limits of human attention.<\/p>\n\n\n\n

      Is Generative CAD Ready for Agents?<\/strong><\/p>\n\n\n\n

      But when will CAD systems draw, or at least autocomplete, our parts for us? We\u2019ve seen success in architectural applications like automated office layout and topology optimization has given us countless lightweight brackets. These victories hint at broader possibilities, but the path to true AI-driven design may run through some familiar territory.<\/p>\n\n\n\n

      Parametric Generative<\/strong><\/p>\n\n\n\n

      Before PLM, we called the mechanical CAD industry mechanical design automation<\/strong>. Starting with Pro\/Engineer<\/strong><\/a>, parametric, history-based CAD tools could produce infinite variations of a design using a parametric recipe. These parametric generative<\/strong> systems encompass computational design, expert systems and anything knowledge-based, procedural, or declarative, including programming languages. Field-driven systems like nTop<\/strong><\/a> achieve superior reliability through implicit modeling, while workflow-focused platforms like Synera<\/strong><\/a> excel at integrating disparate systems.<\/p>\n\n\n\n

      Knowledge-based Engineering<\/strong><\/p>\n\n\n\n

      Outside of mass customization and families of simple components like fasteners and bearings, knowledge-based engineering first appeared in tooling design like molds, dies, jigs and fixtures. Sophisticated experts are needed to program the CAD systems or author reusable \u201ctemplates,\u201d and such investments make economic sense in high-end applications.<\/p>\n\n\n\n

      In the architectural and design world, the computational<\/strong> design paradigm celebrated dataflow models starting with Generative Components<\/strong><\/a>, Dynamo<\/strong><\/a> and Grasshopper<\/strong><\/a>. Most modern architecture and industrial design with thematic, varying patterns result from artists exploring these tools. The \u201cdataflow\u201d environments navigated by computational designers descended from tools used by electronic musicians, such as Max\/MSP<\/strong><\/a> and foster subjective, experimental workflows.<\/p>\n\n\n\n

      \"\"
      Max\/MSP: navigating the void<\/strong><\/a> between DJ and computational designer since 1985.<\/em><\/figcaption><\/figure>\n\n\n\n

      Closed Loop Optimization<\/strong><\/p>\n\n\n\n

      Coupled with simulation, it\u2019s a common practice to run experiments with myriad samples to chart Pareto fronts (optimal sets) for how various parameters affect product fitnesses, or perform a gradient descent to find local optimality, as introduced with PTC<\/strong><\/a>\u2019s behavioral modeling. Leap71<\/strong><\/a>\u2019s pure code approach exemplifies the power of this methodology.<\/p>\n\n\n\n

      Topology optimization offers another optimization path using heuristics to produce optimal parts with simple solvers. nTop adds spatially varying control to parametric modeling through their trademark \u201cfield-driven design,\u201d broadening the practice to more geometries and fitness functions. Machine learning appears poised to revolutionize these optimization technologies, particularly through neural implicits and a better understanding of design context. For example, TOffeeX<\/strong><\/a> is applying this technique to multiphysics systems, in some cases replacing what was done with surrogates just a few years ago. InfinitForm is improving compatibility with CAD representations.<\/p>\n\n\n\n

      Geometric optimization continues to expand beyond individual parts to entire processes. Companies like Atomic Industries<\/strong><\/a> are taking a vertical approach, building optimization knowledge in layers to complete manufacturing pipelines for optimal control over quality and throughput.<\/p>\n\n\n\n

      \"\"
      Leap71\u2019s aerospike engine<\/strong><\/a> appears to use parametric optimization and inverse engineering as part of its intelligence model.<\/em><\/figcaption><\/figure>\n\n\n\n

      Diffusion-based Industrial Design<\/strong><\/p>\n\n\n\n

      Recently, the term generative<\/strong> has gone mainstream in art and graphics design. Why can\u2019t we just diffuse mechanical designs from prompts and sketches?<\/p>\n\n\n\n

      The answer lies in engineering\u2019s sensitivity to small changes: slight geometric modifications can dramatically affect performance, especially when they alter topology. While art models can smooth over such discontinuities, engineering cannot. However, industrial design, the most artistic side of engineering, offers promising territory for diffusion models.<\/p>\n\n\n\n

      Tools like Vizcom<\/strong><\/a> already demonstrate the potential of diffusion for industrial design sketching. While 3D applications remain unexplored, the future seems clear: we\u2019ll soon send the internal components of consumer products and simple machines to AI industrial designers who will generate complete enclosures, including attachment points, fasteners, latches and hinges. Mold shop agents will then generate the necessary tooling. Why not have designer agents in your CAD system, wrapping your chassis with different design suggestions.<\/p>\n\n\n\n

      The Agentic Engineering Ecosystem<\/strong><\/p>\n\n\n\n

      Is a CAD agent just a CAD add-in on steroids, or are new approaches needed to support agentic product development processes? While agents will certainly need access to CAD and PLM APIs, if we treat them as simple services, like file translators, we may miss their transformative potential. The integration of AI agents into engineering workflows raises fundamental questions about architecture, security and process.<\/p>\n\n\n\n

      Data Ethics<\/strong><\/p>\n\n\n\n

      Consider privileges and autonomy: for example, should an interference-checking agent have the authority to automatically edit parts and schedule design reviews? Could it initiate engineering change orders? Software development already embraces automated quality checks that can reject submissions and automatically recommend some categories of minor changes, suggesting a model where agents operate within supervised workflows. The challenge lies in balancing autonomy with control.<\/p>\n\n\n\n

      Access and Security<\/strong><\/p>\n\n\n\n

      Security presents another critical dimension. As agents process proprietary design data to deliver insights, they must do so without exposing intellectual property. AI systems engineering requires careful consideration of security boundaries and data access patterns.<\/p>\n\n\n\n

      Even if the data is available, is it presented in a useful form? How will we analyze and prepare it for training, such as via tokenization? Companies such as KeyWard<\/strong><\/a> help create automated pipelines for moving data into modern data science techniques.<\/p>\n\n\n\n

      Scalability<\/strong><\/p>\n\n\n\n

      The potential for AI agency extends beyond immediate process automation. Just as agents can optimize individual designs, they might learn broader patterns from PLM datasets: predicting delivery times, forecasting costs and identifying process improvements based on how engineering data evolves over time.<\/p>\n\n\n\n

      These questions highlight a broader transformation in engineering practice. As agents handle routine validation, surface relevant data and automate tedious tasks, engineers can explore more design alternatives, validate more scenarios and respond more quickly to manufacturing and supply chain feedback. The result isn\u2019t just improved efficiency; it\u2019s a deeper engagement with the entire value chain from concept to delivery.<\/p>\n\n\n\n

      Looking ahead, we expect existing CAD add-ins to evolve into agent frameworks, becoming specialized agents with well-defined interactions. This transition will make the tacit knowledge encoded in these tools more accessible to automated systems, creating a richer ecosystem where human expertise can be systematically applied at scale.<\/p>\n\n\n\n

      Summary<\/strong><\/p>\n\n\n\n

      The emergence of AI-powered engineering agents represents a fundamental shift in how we approach product development. Rather than waiting for a single unified AI system to revolutionize engineering, we\u2019re seeing a more organic evolution where specialized agents integrate into existing workflows, each addressing specific aspects of the engineering process, gradually transforming engineering from a purely human endeavor into a collaborative process between engineers and AI systems.<\/p>\n\n\n\n

      What makes this transition particularly noteworthy is how it preserves and enhances existing engineering workflows rather than replacing them. These agents work within our CAD, PLM and BIM systems, augmenting rather than disrupting established processes. They\u2019re bringing AI capabilities to engineering in a way that respects the complexity and precision requirements of the field while addressing practical challenges around proprietary data, security and integration.<\/p>\n\n\n\n

      As we look toward 2025 and beyond, the key challenge isn\u2019t just developing more sophisticated AI capabilities, it\u2019s creating the infrastructure and protocols that allow these agents to work together effectively. We need frameworks for agent privileges, security boundaries and supervision that can scale across organizations and industries. The potential impact is significant: faster design cycles, more optimal solutions, reduced errors and ultimately, better products. But realizing this potential requires careful attention to both the technical and organizational challenges of integrating AI agents into engineering workflows.<\/p>\n\n\n\n

      About GCL<\/strong><\/p>\n\n\n\n

      At Gradient Control Laboratories<\/strong><\/a> (GCL), we have the privilege of seeing patterns emerging among the most innovative engineering software startups. Last year, we tracked the rise of differentiable engineering<\/strong><\/a> as the first differentiable CAD and CAE APIs appeared. Now, as we wire AI agents into PLM and BIM architectures, we\u2019re ready to share our expectations for 2025 and beyond.<\/p>\n\n\n\n

      GCL is starting to make systems that host agents to analyze and generate PLM data. The company is aware that many others are working on AI that could present as or host agents. GCL is contemplating a focused, technical workshop in the spring to hammer out solutions to some of the challenges above. If you\u2019d like to participate, please respond here<\/strong><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

      The emergence of AI-powered engineering agents represents a fundamental shift in how we approach product development.<\/p>\n","protected":false},"author":7,"featured_media":2275,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,6],"tags":[],"class_list":["post-2273","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-cad"],"_links":{"self":[{"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/posts\/2273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/comments?post=2273"}],"version-history":[{"count":4,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/posts\/2273\/revisions"}],"predecessor-version":[{"id":2344,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/posts\/2273\/revisions\/2344"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/media\/2275"}],"wp:attachment":[{"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/media?parent=2273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/categories?post=2273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/superwomansolutions.com\/engtechnica\/wp-json\/wp\/v2\/tags?post=2273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}