MathWorks has announced new capabilities that enable AI agents to run and refine MATLAB workflows through two new open-source tools, MATLAB MCP Server and MATLAB Agentic Toolkit, using Model Context Protocol.
With these tools, agents can write MATLAB code, run it in a live session, examine outputs or errors and iterate toward a correct result, while engineers remain responsible for validating results and applying their expertise.
The new capabilities are aimed at MATLAB users, AI engineers building agent-driven workflows and platform teams managing AI-assisted engineering environments. Because agents run code directly in MATLAB, their outputs are based on deterministic computation, numerical analysis and executable models rather than only language-model-generated text. Engineers can check results by reviewing outputs, comparing them against expected behavior and refining workflows through iteration – standard practices in engineering development.
“As organizations adopt agentic AI in model-based design and engineering, the focus is shifting from code generation to reliable execution within established multi-disciplinary toolchains,” said Diego Tamburini, AI practice director at CIMdata.
“Engineers remain responsible for defining problems, validating outcomes and maintaining oversight, while AI agents increasingly handle iterative and repetitive tasks – augmenting human efficiency and effectiveness. This reinforces the importance of human-in-the-loop workflows, where real execution and validation underpin trust in AI-driven engineering processes.”
MATLAB MCP Server and MATLAB Agentic Toolkit are open-source packages, enabling developers and organizations to inspect, extend and incorporate agent‑based workflows with MATLAB in their own environments using agentic tools such as Claude Code, GitHub Copilot, OpenAI Codex and Gemini CLI. This supports interoperability across diverse AI agent frameworks and positions MATLAB as foundational infrastructure for emerging agent‑based engineering ecosystems.
“AI agents are most effective in engineering when they can directly interact with the tools used for design, simulation and analysis,” said Seth DeLand, generative AI product manager at MathWorks.
“By enabling agents to execute and iterate MATLAB workflows, we’re connecting AI-driven iteration to the same computational environment engineers use to develop and validate their work. This allows teams to move from LLM generated code to executable, testable results within a consistent engineering framework.”
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