u/Creative_Sushi

TL;DR:
Agentic AI doesn’t “do systems engineering for you,” but it can seriously speed up the boring parts if it’s tightly scoped to the right tools and workflows.

Key takeaways:

• The setup uses MATLAB, Simulink, and System Composer, with an AI coding agent wired into domain-specific APIs.
• They follow a classic RFLP workflow (Requirements → Functional → Logical → Physical), but let the agent help with:
  • Project setup
  • Creating architecture models and interfaces
  • Managing traceability and boilerplate API calls
• The interaction is conversational (“create a new MBSE project”), but the engineer still makes the architectural decisions.
• This is about reducing friction in MBSE workflows so engineers can focus on reasoning, tradeoffs, and system intent, not about automating their work.

https://blogs.mathworks.com/simulink/2026/04/26/model-based-systems-engineering-and-agentic-ai/

You need to generate a lot of scenarios to test automated driving algorithms and a colleague of mine decided to use AI to assist scenario generation. Check out what he could do with MATLAB Agentic Toolkit and RoadRunner.

Adam Danz published his favorite features in R2026a. Since I already shared the video about web canvas, here is one for sharing apps.

In his blog post, you can also see web canvas in action - you can interact with his plots! https://blogs.mathworks.com/graphics-and-apps/2026/04/27/graphics-and-app-building-updates-in-r2026a/

• Share Interactive Graphics on the Web with Web Canvas
• Share Apps Directly from App Designer with MATLAB Drive
• A More Accessible and Flexible Axes Toolbar
• Visualize Distributions with raincloudplot
• Plot Directly from Table Data
• Increased flexibility when Linking Axes

I would personally be start using table data for plotting going forward!

I found this blog post interesting and worth sharing.

>Model-Based Systems Engineering (MBSE) promises better decisions, stronger traceability, and clearer alignment across teams. But in many organizations, the day-to-day reality still feels fragmented. Requirements may live in one tool. Architecture lives in another. Analysis happens somewhere else. Verification evidence is scattered across reports, scripts, and specialized tools. Teams may have models, but they still struggle to keep engineering data connected, current, and authoritative.

The author argues that SysML v2’s real potential isn’t just a better modeling language, but the shift toward a repository‑centric, API‑driven approach. The idea is that MBSE often breaks down after models exist—when teams try (and fail) to keep requirements, architecture, analysis, and verification connected across multiple tools.

Here is the link to the post.

https://blogs.mathworks.com/digitaleng/2026/04/30/why-mbse-still-breaks-at-the-seams-and-how-sysml-v2-could-help/

You probably noticed that my colleagues and I have been playing with Ai coding agents a lot, and we wanted to share and make freely available what we learned so far in a simplest and easiest way possible.

MATLAB Agentic Toolkit was released a few weeks ago.

• Makes installing MCP Server and agent skills really easy - no more tinkering necessary - see the video.
• Together, it streamlines your working experience with those AI agents and you can become productive much faster.
  • MCP Server lets AI agents talk to MATLAB and actually run code, execute tests, and inspect results instead of guessing
  • Agent Skills teach AI agents MATLAB-specific workflows, conventions, and best practices - leading to more idiomatic, efficient, and higher‑quality code with less token waste (I also contributed a skill 😊)
• Open source and actively updated, available on GitHub

For Simulink users, check out the companion Simulink Agentic Toolkit.

Learn more from u/MikeCroucher's post https://blogs.mathworks.com/matlab/2026/04/13/introducing-the-matlab-agentic-toolkit/

I often see people asking questions about Simulink models they see on paper, wanting to reconstruct it from an image. A colleague of mine figured out how to do it with Claude Code.

He gave a diagram of a spring-mass-damper system and asked Claude to build it in Simscape.

• Claude identified the components from the image: spring (k), damper (b), mass (m), applied force F(t), and displacement output X(t)
• It programmatically searched the Simscape Foundation Library to resolve correct block paths — including handling newline characters in Simulink library names
• Created the model, added 11 blocks, set default parameters (m=1 kg, k=100 N/m, b=5 N·s/m), and wired all 13 physical and signal connections
• Ran the simulation and verified the expected underdamped response (ζ=0.25, ωn=10 rad/s)

The entire workflow — from image interpretation to a running simulation — was done through natural language prompts in the terminal.

The key enabler here is the MATLAB MCP server, which gives Claude Code direct access to evaluate MATLAB commands, inspect libraries, and build Simulink models in a live MATLAB session. It is not a replacement for engineering judgment but maybe the initial block diagram can be converted into the model quickly.

To learn more, check out MATLAB Agentic Toolkit https://www.mathworks.com/products/matlab-agentic-toolkit.html