Empowering Embedded Software Development with AI-Driven Toolchains

Large Language Models (LLMs) are shifting from chat assistants to tool-connected engineering agents. For embedded software development teams, the key is integrating AI into existing toolchains without weakening safety, security, or compliance.

This talk gives a practical overview of AI-assisted embedded development and shows how the Model Context Protocol (MCP) and reusable “skills” connect an LLM to requirements, implementation, on-target tests, and static/dynamic analysis in a closed loop: generate → verify → refine. A case study on a state-of-the-art automotive microcontroller achieved performance improvement while preserving MISRA compliance and improving code coverage.

Speaker

Zachary Cole

Principal Field Application Engineer, TASKING

Having worked his way up to Principal Field Application Engineer, Zachary is currently in his 9th year with TASKING. A Liverpool Hope University graduate, he works heavily within the automotive, aerospace and industrial sectors. Helping to bring specialty knowledge of the TASKING tools to a wide variety of users for both safe and secure embedded applications.