Agentic AI in Simulation: Foundations & First Use Case

Agentic AI is one of the most discussed topics in engineering, yet most simulation engineers have limited visibility into what it is and what it can do for their daily work.

This two-part series, presented by FEA Academy in collaboration with Synera, closes that gap. Part 1 establishes the foundations, introduces the Synera platform, and walks through a live workflow of a realistic FEA task.

Key takeaways:

• What agentic AI is, and how it differs from scripting, macros and traditional automation
• How agents support engineers across the simulation workflow, and where engineering judgment stays essential
• What implementation looks like in practice, seen in two live workflows on realistic engineering tasks

The two live workflows:

Introducing the Synera "Stack" platform

How a real multi-agent system is built. We walk through the Synera stack, the layered architecture running from deterministic workflows up through Synera Run and the multi-agent system to a single chat interface, and draw the line between rule-based workflows, workflows with some agentic capability built in, and agents acting autonomously on top of those automations.

First Use Case: Text to Flexure for NASA

How far can AI agents go in engineering design, and can we trust what they produce? A team of AI agents takes a spacecraft instrument's geometry and a plain-language description of its requirements, and designs the mount that holds it. Like an experienced engineer, the agents use judgment to decide what to change next, reaching a good design in a handful of iterations instead of hundreds of brute-force runs. Deterministic Synera workflows build, analyze and check every design along the way.

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