SimScale platform

The architecture of agentic engineering

SimScale unites cloud-native simulation, Engineering AI, and Physics AI into a single platform that orchestrates every stage of engineering design from exploration to validation. The only AI-native agentic engineering platform that can explore 1000’s of design decisions in seconds, across any simulation, workflow, or toolchain.

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Trusted by 900,000+ engineers worldwide

Modern engineering requires more than faster simulation. It requires systems that capture expertise, learn and automate engineering workflows. SimScale’s architecture powers agentic engineering by combining multiphysics simulation, AI models, and cloud infrastructure into a unified, open environment.

Technology overview

Four layers power SimScale’s agentic engineering platform — orchestration, simulation, integration, and cloud infrastructure.

Orchestration layer

Orchestrating engineering workflows. Coordinates users, templates, AI agents, and data into guided and automated simulation workflows. Ensures repeatability, governance, and collaboration across engineering teams.

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Simulation & AI engine

Where physics and AI converge. Combines validated multiphysics solvers with Physics AI to deliver high-fidelity insights at unprecedented speed.

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Open Integration

Through the API and Workflows, third-party solvers, custom AI models, and proprietary methods run natively on SimScale’s infrastructure — not as external integrations, but as native participants fully accessible to Engineering AI agents for autonomous orchestration across your engineering stack.

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Cloud infrastructure

Elastic simulation infrastructure. Secure cloud computing and unified data management provide the scale required for large simulations and AI training workloads.

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Related resources

Blogs Public Projects Validation Cases Webinars & Events Reports Press

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Tribal knowledge in engineering: how to capture 40 years of design decisions before they walk out the door

What tribal knowledge is, where it hides in engineering teams, and a five-step method to capture design decisions as validated, traceable context for AI agents.

Peter Selmeczy

November 2, 2026

Engineering AIProduct

Text to CAD to simulation and optimization for manufacturing

Why text to CAD falls short for manufacturing, how the Onshape FeatureScript MCP turns prompts into reusable parametric tools, and how the SimScale agent tests them.

Peter Selmeczy

October 30, 2026

Engineering AIProduct