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Jacob Beningo explores the shift from using AI as a coding assistant to agentic firmware development, where AI agents autonomously build, debug, test, and verify embedded systems. He shares a real experiment using an NXP MCX N947 board and an AI-written I2C driver. He also covers hardware safety guardrails, simulation, and how embedded engineers can evolve into systems engineers.
Key Takeaways:
• Most teams use AI only as a coding assistant. The bigger gains come from agentic workflows where AI builds, debugs, tests, and verifies firmware.
• AI doesn't get firmware right on the first try, and neither do human engineers. It needs the ability to iterate, as with test-driven development.
• Connecting an AI agent to real hardware or a simulator lets it observe results, find root causes, and fix problems instead of guessing.
• In the I2C experiment, the agent correctly diagnosed a hardware problem (a backwards click board) that the engineer had wrongly blamed on the software.
• Once the board was fixed, the agent produced a tested, production-grade temperature and humidity sensor driver within about an hour.
• A robust agentic workbench combines skills, agents, RAG, and MCP servers, with an orchestration agent following your own development processes.
• Give agents access to debuggers, oscilloscopes, logic analyzers, and adjustable power supplies. This brings real risk, including fire hazards, hardware damage, and liability.
• Build safety into MCP servers. For example, require users to set voltage and current limits up front, and prompt for approval whenever the agent tries to exceed them.
• Simulation offers a safe, continuous way to validate AI-generated code, especially when hardware is dangerous to leave unattended.
• Start small. Pick a repetitive daily task with a clear outcome, automate it, then expand the workflow gradually while keeping human verification and static analysis in place.
Key Takeaways:
• Most teams use AI only as a coding assistant. The bigger gains come from agentic workflows where AI builds, debugs, tests, and verifies firmware.
• AI doesn't get firmware right on the first try, and neither do human engineers. It needs the ability to iterate, as with test-driven development.
• Connecting an AI agent to real hardware or a simulator lets it observe results, find root causes, and fix problems instead of guessing.
• In the I2C experiment, the agent correctly diagnosed a hardware problem (a backwards click board) that the engineer had wrongly blamed on the software.
• Once the board was fixed, the agent produced a tested, production-grade temperature and humidity sensor driver within about an hour.
• A robust agentic workbench combines skills, agents, RAG, and MCP servers, with an orchestration agent following your own development processes.
• Give agents access to debuggers, oscilloscopes, logic analyzers, and adjustable power supplies. This brings real risk, including fire hazards, hardware damage, and liability.
• Build safety into MCP servers. For example, require users to set voltage and current limits up front, and prompt for approval whenever the agent tries to exceed them.
• Simulation offers a safe, continuous way to validate AI-generated code, especially when hardware is dangerous to leave unattended.
• Start small. Pick a repetitive daily task with a clear outcome, automate it, then expand the workflow gradually while keeping human verification and static analysis in place.
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