Agentic AI is everywhere. It shows up in vendor keynotes, LinkedIn feeds, conference agendas and board presentations. Every week, a new platform promises digital engineering coworkers and simulation pipelines that run themselves.
And yet, when I talk with simulation engineers, the conversation goes in a very different direction.
Over the past months, I have had many of these conversations with analysts, team leads and engineering managers. The same pattern keeps appearing: strong curiosity, limited visibility, and very few clear answers.
The questions nobody is answering clearly
Engineers ask practical questions. What is an agent, really? Is it just a script with a language model inside? How is it different from the macros and automated workflows we already use? And the one I hear most often: if an agent sets up the model and runs the solver, what is left for me?
Managers ask different questions, and they are just as unanswered. Where do we start? Do we have to replace the tool chain we spent years building? How do we validate what an agent produces, and who is accountable when it is wrong? What happens to our data and our IP?
The answers they find are often vague, or wrapped in marketing language. That is not good enough for a profession built on verification. We do not accept a result because the contour plot looks nice. We should not accept a technology because the demo looks impressive.
Why I organize this series
This gap is the reason I decided to organize Agentic AI in Simulation, a free two-part webinar series in collaboration with Synera.
The goal is simple: demystify agentic AI in FEA, in clear and practical terms, without the hype. Two sessions, each answering one question:
What is agentic AI in FEA?
And how do we make it work in a real engineering organization?
I want to be clear about my position from the start. Agentic AI is not a replacement for the analyst. Properly understood and properly governed, it lets engineers spend less time on repetitive execution and more time on the decisions that matter: assumptions, interpretation and design direction.
Part 1: Foundations and First Use Case
Tuesday, October 27, 2026 | 8:00 AM PDT | 11:00 AM EDT | 4:00 PM CET |
The first session builds the foundations:
What an agent is
Why it is not the same thing as a script, a macro or a workflow.
What it can do inside an FEA workflow, from geometry cleanup and meshing to solver runs and reporting.
Where the analyst stays in the loop.
Synera will then run a live demonstration: a realistic FEA task, handled end to end by an agent, in a workflow most analysts will recognize.
I will not give away the full framework here. But if you leave with one idea, it should be this one: automation follows instructions, while an agent pursues a goal and knows when to hand back control.
Part 2: The Practitioner’s Perspective
Thursday, November 12, 2026 | 8:00 AM PST | 11:00 AM EST | 5:00 PM CET |
The second session addresses the two questions that decide whether agentic AI ever leaves the pilot stage.
The first is the position of the engineer. What can the agent own, and what must stay with the engineer? When does the agent hand back control, and with what information? And the question every skeptical analyst asks, rightly: can I trust what the agent did? My short answer is that trust comes from the process, not from the tool. We will see what that means in practice.
The second is implementation. How do you bring agents into an organization without tearing out the CAD, CAE and PLM tools already in place? Which process should you start with? And what does it take beyond the technology, in validation practice, IT and data, and governance?
Our guest, Abhinav Tanksale, CAE Engineer at My Physics Cafe, used agentic AI in real simulation work. He will share what changed, what surprised him, what he was skeptical about, and where he still had to step in. Synera will also show a second, contrasting use case at a different stage of the workflow.
Who should join
The series is for simulation engineers who want to understand what agentic AI means for their daily work, and for engineering managers who need to decide whether, where and how to implement it. No prior experience with AI is required. A solid understanding of the FEA workflow is enough.
Both sessions are free, live on Zoom, with time reserved for your questions. You can register now, where you will find all the details:
The technology is moving fast. Engineering judgment is still what makes a result trustworthy. Let’s make sure we understand the first well enough to protect the second.
See you on October 27.
Dominique Madier
| FEA Academy Founder & Director |
| FEA Consultant | FEA Trainer and Coach | Book Author | Speaker |
| Helping Students and Engineers to Master FEA |
Surrogate modeling is one place to spend more of your new found time. I wrote about it here: https://burhop.substack.com/p/ai-surrogates-in-cfd-and-fea
The shift toward automated engineering models forces us to redefine human value as pure oversight rather than manual execution. When the machine handles the mechanics, the engineer becomes the architect of intent and the final arbiter of safety. This transition mirrors the evolution of safety equipment in hazardous environments. We stop measuring the cost of the gear against a hypothetical failure and start treating it as the baseline for operations. Security teams must adopt this mindset because the tools are becoming the environment itself. By shifting our focus from the output of the model to the integrity of the system design, we move from being operators to being guardians of the process.
https://cyrilsimonnet.substack.com/p/nobody-asks-for-the-roi-of-a-hard?utm_source=substor&utm_medium=substack&utm_campaign=comment