For decades, the development of Micro Controller Unit (MCU) firmware has remained one of the most stubborn bottlenecks in hardware engineering. While high-level software development has been transformed by AI assistants, embedded systems require a tight, physical coupling between code and silicon. The traditional cycle is grueling: a developer writes a few lines of C, flashes the chip, observes a crash, and spends hours with a logic analyzer to find a timing error. This manual loop of trial and error is where productivity goes to die, as the AI cannot see the hardware and the hardware cannot tell the AI why it failed.
The Blueprint for Autonomous Firmware
Samsung Electronics has recently challenged this paradigm by verifying a system called Harness Engineering, a framework designed to allow AI agents to develop MCU firmware autonomously without human coding intervention. To test the viability of this approach, Samsung targeted the firmware for a range hood product, applying a recursive cycle of planning, implementation, and verification. The results indicate a significant shift in how embedded software can be produced. Across five separate test runs, the AI agent successfully completed the firmware development autonomously in every instance. Each cycle took between 4.5 and 5.5 hours to complete, reaching a functional completion rate of approximately 95% for basic operations.
The remaining 5% of the development gap occurred primarily within the Hardware Abstraction Layer (HAL). Specifically, the AI struggled with components that require deep physical hardware verification, such as UART, Timer, WatchDog, and Clock configurations. These gaps were not catastrophic; they required a human developer to step in for roughly 1 to 4 hours of additional debugging to finalize the hardware-specific optimizations. Despite this, the overall impact was stark. Samsung confirmed that the pure development time could be reduced by an average of 50% to 70% compared to traditional manual workflows.
To achieve this, Samsung deployed a sophisticated technical stack. The intelligence was driven by Codex AI running on a PC, which was interfaced with the physical hardware via a JTAG-based MCU debugger. To solve the problem of hardware hangs—a common occurrence in firmware development where the MCU becomes unresponsive—Samsung integrated a USB Switch capable of physically controlling the 220V power supply. This allowed the AI to perform a hard power cycle of the device, ensuring the autonomous loop could recover from critical crashes without human assistance.
From Code Writing to Harness Design
The fundamental breakthrough of Harness Engineering is that it does not attempt to make the AI a better coder through parameter tuning or prompt engineering. Instead, it treats the entire development environment as a programmable entity. The core philosophy is the creation of a Single Source of Truth, where all necessary information is centralized in a `docs/` folder. The AI does not guess the intent; it references a structured environment consisting of specific directories and definition files. The `behavior/` folder contains detailed product operation specifications, while the `design/` folder houses the underlying logic and rationale. Hardware configurations and initialization data are stored in `hardware/`, and the AI's operational boundaries are strictly governed by `AGENTS.md` and `ARCHITECTURE.md`, which define the layer structures and dependency rules.
This environment powers the AUTOPILOT loop. Starting from zero-base code, the system iterates through planning, implementation, and verification. To prevent the common AI failure mode of self-confirmation bias—where a model convinces itself that buggy code is correct—Samsung decoupled the process into two distinct roles: a generation agent and an evaluation/verification agent. The implementation is further constrained by a combination of Specification Driven Development (SDD), Test Driven Development (TDD), and Behavior Driven Development (BDD). For any piece of code to progress, it must pass through three rigorous quality gates: Build, Test, and Lint.
The most critical innovation is the hardware verification pipeline. The Codex AI directly controls the JTAG debugger and the USB Switch. After analyzing the specifications to derive test scenarios, the AI uses Memory Write commands via the debugger to inject key inputs directly into the MCU memory. It then performs a Memory Read to extract the state values and autonomously determine if the scenario passed or failed. If the firmware enters a hang state, the AI triggers the USB Switch to cut and restore 220V power, resetting the set to a clean state before attempting a new iteration.
This shift fundamentally redefines the role of the embedded engineer. The developer is no longer a code writer but a Specification and Harness Designer. In this new regime, the guiding principle is that any specification the AI cannot verify effectively does not exist. If a developer fails to document whether a fan speed should be controlled via a Low-Mid-High toggle or a simple On-Off switch, the AI will make an arbitrary decision, leading to a verification failure. Consequently, the engineer's primary value now lies in transforming legacy documentation and tacit knowledge into formalized, machine-readable documents that the AI can utilize.
While the 95% autonomy rate is impressive, the HAL limitations highlight a persistent reality: AI cannot yet fully simulate the physical nuances of hardware. The intricacies of UART timing or clock synchronization remain the domain of the human expert. The AI can handle the vast majority of the logic and structural implementation, but the final 5% of hardware optimization still requires a seasoned engineer's intuition and diagnostic skill.
Transitioning to this model requires more than just deploying an LLM; it demands a heavy initial investment in establishing a perfect verification standard. The goal is to build a harness so robust that human code review becomes redundant, shifting the engineering effort from the act of writing to the act of defining.
