AI is rapidly advancing from simple chatbots to autonomous agents capable of performing actions and using tools to achieve objectives set by a user. This evolution is the unlock that engineers have been waiting for. Unlike chatbots, AI agents can interact with your engineering data, make decisions, and carry out actions—all with minimal oversight.
An agentic Engineering AI is in active development at SimScale, integrated directly into our simulation platform, and we have customers using it today in alpha testing. This agent is set to make simulation even more accessible, intelligent, and productive, regardless of your experience level.
In this article, we dive into five agentic workflows that we see Engineering AI unlocking, either today or in the near future. The technology is maturing fast and we are excited about what’s coming next!
1. Helping Novice Users Get to a Working Simulation Sooner
Making simulation approachable for new users has always been at the core of SimScale’s mission. Engineering AI agents turbocharge this democratization. Rather than relying on static documentation or guides, an agent understands your CAD model and context in real time. It can proactively guide you step-by-step toward a viable setup—automatically diagnosing missing inputs, suggesting best practices, and flagging potential challenges long before you hit “run.” This transforms onboarding from a knowledge hurdle into a guided, confidence-building experience.
2. Accelerating Model Setup With Intelligent Automation
Setting up a simulation is often time-consuming, particularly when it comes to assigning boundary conditions, materials, or physics models. SimScale’s Engineering AI leverages both geometric information and vast simulation knowledge to streamline this process. The agent recognizes context from your geometry and model setup, recommends or auto-applies suitable settings, and raises issues only when human judgment is needed. The result: faster, more consistent model preparation with less context switching or uncertainty—bringing value to novices and seasoned analysts alike.
3. Guiding the Application of Company Best Practices
Scaling simulation quality and consistency across a distributed team remains an industry pain point. Engineering AI can actively reinforce organizational best practices. It serves as a digital mentor, surfacing internal guidelines and guarding against common pitfalls as users progress through a workflow. Over time, such agents learn from recurring mistakes or successes, driving incremental improvement and consolidating hard-won expertise—no matter the user’s prior experience.
4. Collaborating With Other Agents
Some of the most exciting potential of Engineering AI agents emerges through collaboration—between agents, not just humans. Multiple specialized agents, each with domain-specific reasoning, can work together across complex engineering challenges. Recently, during a joint webinar with Generative Engineering, we demonstrated a proof-of-concept agent-to-agent workflow. Here, SimScale and Generative Engineering agents jointly orchestrated a design space exploration, translating broad goals into practical analyses and autonomously iterating on concepts in the background. This kind of digital teamwork could transform the status quo of tool interoperability and ultimately deliver seamless, agent-powered multi-tool engineering workflows in the near future.
5. Automating RFQ Responses
One high-impact future application of the workflow demonstrated above is automating RFQ (Request for Quotation) responses. Mature engineering agents will soon be able to ingest customer requirements, map them to relevant design specs, set up and run appropriate simulations, and validate that the proposal meets performance criteria—all automatically. For organizations handling large volumes of RFQs or routine design studies, this unlocks significant value: reducing manual workload, speeding up response times, and freeing engineers to focus on high-impact, creative tasks.
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Looking Ahead
Engineering AI agents represent a step change for simulation-driven design. They reduce ramp-up times, accelerate core workflows, promote best practices, enable multi-agent collaboration, and automate entire processes. At SimScale, we’re building these capabilities directly into our platform so that more organizations and engineering teams can benefit from agentic workflows.
If you’re interested in what AI-accelerated engineering could do for your team, get in touch with us below and be part of this new era of digital engineering.
