Generated C Code for MCU Agents — Spec to Firmware

// last reviewed 2026-05-22 · Marcus Rüb

Generated C Code for MCU Agents

Agent-as-codegen means a developer writes a high-level specification — in YAML, JSON, or a visual flow editor — and a tool generates the corresponding C (or Rust, or Zig) firmware module: sensor drivers, state machine, MQTT client configuration, inference pipeline scaffolding, and OTA hooks included.

This is not a new idea. STM32CubeMX has generated HAL initialization code since 2014. Edge Impulse has generated inference C libraries since 2019. The extension to MCU agents is generating the full agent loop, not just one component of it.

How does traditional firmware development work?

In traditional firmware development:

Developer writes HAL initialization for each peripheral.
Developer hand-codes the application loop, state machine, protocol client.
Developer integrates any ML model inference library manually.
If another developer takes over or requirements change, the structural conventions (naming, state machine layout, MQTT topic scheme) may diverge.

This is not inherently wrong — for novel hardware or custom protocols, hand-coding is necessary. The cost is time to first working prototype and the lack of standardized structure.

What does agent codegen replace?

A codegen approach takes a specification like this:

# agent-spec.yaml — MCU agent definition
agent:
  id: "esp32s3-factory-humidity"
  board: "esp32-s3"
  rtos: "freertos"

sensors:
  - id: sht31
    interface: i2c
    address: 0x44
    sample_rate_ms: 500
    outputs: [temperature_c, humidity_pct]

triggers:
  - name: humidity_high
    condition: "humidity_pct > 75.0"
    action: publish_event
    topic: "agents/{id}/event"
    qos: 1

  - name: temp_anomaly
    condition: "model.score('anomaly') > 0.85"
    action: [publish_event, actuate_relay]
    relay_pin: GPIO_NUM_4

communication:
  broker: "mqtts://broker.example.com:8883"
  ca_cert: "certs/ca.pem"
  device_cert: "certs/device.pem"
  device_key: "certs/device.key"

model:
  source: "edge_impulse"
  project_id: 12345
  format: "eon_c"

And generates:

agent_main.c — FreeRTOS task setup, queue definitions, main entry.
sensor_sht31.c/h — I2C driver wrapper and sample function.
agent_state_machine.c/h — State machine with the trigger conditions as transition guards.
mqtt_client.c/h — Broker config, TLS setup, publish/subscribe wrappers.
inference_wrapper.c/h — Edge Impulse EON library integration.
CMakeLists.txt or Makefile — Build system for ESP-IDF or Zephyr.

What are the benefits?

Benefit	Detail
Time to first prototype	From days to hours for standard sensor + MQTT + threshold agents
Structural consistency	All generated agents follow the same naming and layering convention
Easier fleet management	Codegen tools can regenerate code when spec changes; diff is traceable
Reduced integration bugs	Driver/state machine/protocol wiring is generated, not manually copied
Spec as documentation	The YAML/JSON spec is both human-readable and machine-executable

Where does codegen fall short?

Limitation	Detail
Novel hardware	Unusual sensors or bus configurations may not have driver templates
Custom protocols	Non-MQTT protocols (CAN, Modbus, proprietary RF) are rarely supported
Real-time tuning	ISR priority, DMA channel assignment, cache coherency — still manual
Debugging generated code	Generated code is often harder to read than idiomatic hand-written C
Vendor lock-in	Some codegen tools produce code tied to their proprietary runtime

Adjacent codegen tools in the ecosystem

Edge Impulse SDK / EON compiler

Edge Impulse’s build pipeline is codegen for the ML inference portion of an agent. You define your data pipeline (MFCC, spectral features, raw sensor), train a model in the studio, and deploy via:

edge-impulse-deploy --type arduino    # Arduino/C++ library
edge-impulse-deploy --type espressif  # ESP-IDF component
edge-impulse-deploy --type stm32-cube # STM32CubeMX project

The output is a C/C++ library with your model’s DSP pipeline, quantized weights, and run_classifier() entry point. The EON compiler additionally generates pure C (no dynamic allocation), eliminating the TFLM runtime overhead. This covers the inference layer; the rest of the agent still needs to be written or generated separately.

STM32Cube.AI / STM32Cube AI Studio

ST’s toolchain generates:

Optimized C model inference code for STM32 targets.
CMSIS-NN accelerated kernels matched to the specific STM32 device.
Memory layout analysis showing activation buffer size and static weight size.
Integration with STM32CubeMX project structure.

Like Edge Impulse, it covers inference codegen, not the full agent loop.

Zephyr Device Tree + Kconfig

Zephyr’s Device Tree (DTS) is a form of codegen: hardware topology (I2C buses, SPI peripherals, GPIO banks) is described in .dts files and processed at build time into C header constants and driver bindings. It is not agent-level codegen, but it eliminates hand-coding the hardware abstraction layer for any board with a Zephyr BSP.

ForestHub.ai

ForestHub.ai’s platform extends the codegen approach to the full agent: the spec (sensors, triggers, inference, MQTT topics, delegation rules) is defined at the fleet level, and generated C modules are pushed to device build pipelines. Agent registry, OTA versioning, and multi-board support are part of the platform.

A short generated state machine example

The core of a generated agent — the transition function — should be simple enough to audit:

/* Generated by agent-codegen v1.0 — DO NOT EDIT MANUALLY */
/* Source: agent-spec.yaml (sha256: a3f7c2...) */

AgentState_t agent_transition(AgentState_t state,
                               const SensorData_t *s,
                               const InferenceResult_t *inf) {
    switch (state) {
        case AGENT_IDLE:
            if (s->humidity_pct > 75.0f)
                return AGENT_ALERT_HUMIDITY;
            if (inf != NULL && inf->anomaly_score > 0.85f)
                return AGENT_ALERT_ANOMALY;
            return AGENT_IDLE;

        case AGENT_ALERT_HUMIDITY:
        case AGENT_ALERT_ANOMALY:
            /* Stay in alert until condition clears */
            if (s->humidity_pct <= 70.0f && 
                (inf == NULL || inf->anomaly_score < 0.60f))
                return AGENT_IDLE;
            return state;

        default:
            return AGENT_IDLE;
    }
}

The spec’s trigger conditions become if guards. The hysteresis (75.0 alert, 70.0 clear) is defined in the spec and generated here. A developer reviewing this can trace every condition back to the spec without reading through sensor driver code.

FAQ

Q: Does generated code run slower than hand-written code? Sometimes. Generated code may include unused branches or over-general data structures. For tight timing loops (ISRs, DMA callbacks), hand-optimized code is usually better. The agent state machine and communication layer are not timing-critical in most designs and tolerate generator overhead fine.

Q: How do I add a sensor that the codegen tool doesn’t know about? Most tools support custom sensor driver injection: you write a driver conforming to the expected interface (a sensor_read() function with a defined signature), and the codegen tool wraps it. The custom driver is excluded from code regeneration.

Q: Is there an open standard for MCU agent specs? Not yet as of 2026. There are candidate standards (Matter device definitions, WoT Thing Descriptions) but none cover the full agent spec including ML inference, delegation rules, and MQTT topic hierarchy.

Q: Can generated code be used in safety-certified products? With difficulty. IEC 61508 and ISO 26262 require traceability from spec to code. Some codegen tools support generating traceability artifacts. The code itself must pass the same static analysis and review as hand-written code.