{"id":19084,"date":"2026-02-09T17:04:18","date_gmt":"2026-02-09T22:04:18","guid":{"rendered":"https:\/\/blogs.mathworks.com\/deep-learning\/?p=19084"},"modified":"2026-02-18T09:56:25","modified_gmt":"2026-02-18T14:56:25","slug":"automate-the-documentation-of-your-matlab-analysis-with-agentic-ai","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/deep-learning\/2026\/02\/09\/automate-the-documentation-of-your-matlab-analysis-with-agentic-ai\/","title":{"rendered":"Automate the Documentation of Your MATLAB Analysis with Agentic AI"},"content":{"rendered":"
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\"\"<\/td>\r\nCo-author: Rohan Amarapurkar<\/a><\/strong>\r\n
<\/h6>\r\nRohan Amarapurkar<\/span> is the product manager for the MATLAB Desktop and Editor. Rohan holds a Master's in Electrical Engineering from USC and previously was a UX Researcher at MathWorks.<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
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This is a scenario that I shared with Rohan to get his advice: you finish an analysis in MATLAB, the plots look great, and now you need to share it with your team. So you start the tedious dance\u2014export to PDF, copy the figures, write up a README, hope you didn't forget anything. And then someone asks you to tweak one parameter, and the whole process starts again.<\/div>\r\n
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There is now a more elegant solution, and in this blog post I am going to introduce a workflow that lets AI do the heavy lifting. With the MATLAB MCP Core Server<\/a> and a few well-crafted prompts, you can go from analysis to a beautifully documented GitHub repository in minutes.<\/div>\r\n
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What's new<\/div><\/td>\r\n
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Why it matters<\/div><\/td>\r\n<\/tr>\r\n
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AI-driven documentation workflow using MCP<\/div><\/td>\r\n
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Your analysis, figures, and narrative stay in sync automatically<\/div><\/td>\r\n<\/tr>\r\n
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Plain-text Live Script format (R2025a)<\/div><\/td>\r\n
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Finally, meaningful Git diffs for Live Scripts<\/div><\/td>\r\n<\/tr>\r\n
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Export to Markdown with embedded outputs<\/div><\/td>\r\n
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GitHub renders your analysis as a professional README<\/div><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
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The documentation problem<\/h3>\r\n
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Let's admit it: documentation is often an afterthought. We write code, iterate on our analysis, and by the time it works, we're ready to move on. But good documentation is what separates a throwaway script from a reusable, shareable piece of work\u2014especially if you're a researcher sharing publications, a student building your portfolio or an engineer looking to share your analysis with a colleague (or maybe your future self).<\/div>\r\n

Starting point: your existing analysis<\/h3>\r\n
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Most of us don't start from a blank page. We have a script that already works. Let me show you what I mean. Here's a simple analysis of the power outage dataset that ships with MATLAB (outages.csv<\/span>):<\/div>\r\n
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It works. The plots look great. But it's just a script \u2014 no headings, no explanation of what we're finding, no narrative that a colleague could follow. This is where the MATLAB MCP Core Server comes in.<\/div>\r\n

Why plain-text Live Scripts change the game<\/h3>\r\n
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Before we dive into the workflow, let me explain why it's possible now, and why Rohan's team was instrumental in enabling it.<\/div>\r\n
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When we talked to Live Script users, one pain point came up again and again: the binary .mlx<\/span> format didn't play well with modern development workflows. You couldn't get meaningful diffs in Git. You couldn't open your scripts in VS Code or your favorite text editor. And if the file got corrupted, you were often out of luck.<\/div>\r\n
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With the new plain-text format introduced in R2025a, Live Scripts are now stored as .m<\/span> files with Markdown-based markup in comments. Here's what that looks like:<\/div>\r\n
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The key benefits:<\/div>\r\n