AI is reshaping how engineers work, becoming an integral part of design and development workflows. It is being used to develop and deploy models to embedded devices, improve productivity with generative AI (GenAI), and accelerate engineering workflows with AI agents. MATLAB and Simulink help you apply these capabilities across the entire development process, from concept to deployment.
Generative and Agentic AI in Engineering Workflows
Generative AI assists with tasks such as writing code, explaining complex systems, generating test cases, and creating documentation. By helping to automate routine work, GenAI lets you focus on higher-level design and problem-solving. Agentic AI builds on these capabilities by operating across multi-step workflows. You define objectives and constraints, while AI agents orchestrate tasks such as model creation, analysis, and refinement.
MATLAB and Simulink support the use of this technology in engineering workflows. MATLAB Copilot and Simulink Copilot provide integrated generative AI capabilities. MATLAB Agentic Toolkit and Simulink Agentic Toolkit connect your AI Agents to MATLAB and Simulink, so they can run code, build models, inspect outputs, simulate behavior, and support engineering review.
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Embedded AI for Engineered Systems
Before the rise of generative and agentic AI, engineering applications using AI primarily focused on machine learning and deep learning models. MATLAB and Simulink have long supported this workflow and continue to support it today. You can prepare data, train models, simulate system behavior, and deploy AI algorithms to CPUs, GPUs, and embedded hardware, all within a single environment. This integration helps ensure that AI models are not only accurate, but also meet system-level requirements for performance, safety, and reliability.
You can also incorporate models developed in Python-based deep learning frameworks such as TensorFlow and PyTorch. This approach enables you to reuse existing work and bring these models into simulation, testing, and deployment workflows alongside the rest of your system design.
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Examples of AI Applications
Explore how generative, agentic, and embedded AI support specific engineering workflows.
AI with Model-Based Design
Apply AI techniques to the design of engineered systems.
- Integrate and simulate AI models with systems
- Achieve safety and reliability of AI-enabled systems in operation
- Generate code from AI models to target different hardware
- Manage deployment trade-offs of embedded AI
AI for Robotics
Apply AI to enable autonomy in robotics applications.
- Generate synthetic training data
- Accelerate motion planning and controls
- Identify objects and mapping
- Test and deploy systems
