Edge Computing
Run AI inference and hardware control directly on embedded boards.
Challenge
Sending every sensor frame to the cloud burns bandwidth, adds latency, and stops working when the network does. But running agents on a $5 MCU used to mean writing C by hand.
With afrog.ai
afrog.ai compiles agent policies down to the target — ESP32, RP2040, nRF52, or Jetson — so the same definition runs on silicon with offline fallback and OTA updates.
Example run
agent run · device / field-deployment / node-047
$
The loop
01 · Compile
Agent policy → ESP32-S3 firmware (1.8 MB)
02 · Read
Accelerometer @ 1kHz, FFT on-device
03 · Decide
TFLite Micro anomaly model runs locally
04 · Act
Send LoRa alert only if anomaly confirmed
Put AI in control of your edge computing.