EPISODE #028 – From Coding Assistants to Autonomous Engineering

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.

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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.

Chapters

• 00:00 Introduction: From Coding Assistants to Agentic Firmware
• 02:35 Why AI Skeptics Are Wrong: Workflows and Work Queues
• 04:20 Giving AI Iteration Through Hardware and Simulation
• 05:58 Experiment: NXP MCX N947 and an AI-Written I2C Sensor Driver
• 08:55 The Backwards Click Board and a Production-Grade Driver
• 09:49 The Agentic Firmware Workbench: Skills, Agents, RAG, and MCP
• 10:27 Giving Agents Observability: Debuggers, Scopes, and Power Supplies
• 11:40 Why Agentic AI Helps Time-Starved Teams and the Junior Engineer Concern
• 13:28 Risks of Hardware Access and the Role of Simulation
• 15:16 Boundary Conditions and Safety Guardrails in MCP Servers
• 18:14 Human Oversight, Verification, and Moving Beyond Low-Level Drivers
• 20:34 How to Start: Automate One Repetitive Task at a Time
• 22:00 Closing Thoughts, Resources, and Newsletter

Keywords

agentic firmware, embedded AI, AI in embedded systems, firmware engineering, AI coding assistant, autonomous engineering, MCP servers, RAG, AI agents, NXP MCX N947, I2C driver, test-driven development, hardware-in-the-loop, simulation, embedded systems engineer, AI workflows, firmware automation, Jacob Beningo, Embedded Bytes, safety guardrails