Build edge AI agents with the AWS IoT Greengrass Component SDK for Rust

This article describes building edge AI agents using the AWS IoT Greengrass Component SDK for Rust, demonstrating a water utility pump station use case that classifies sensor anomalies locally and escalates complex cases to the cloud.

• Rust SDK provides sub-0.5 MB runtime footprint versus 30 MB for Python, enabling AI on devices with tight memory budgets (under 256 MB RAM)
• Local ONNX-based classification achieves sub-200 ms latency for anomaly detection without cloud dependency
• Offline-first pattern queues escalation messages locally during network outages and drains them on reconnect
• Complex anomalies escalate to Amazon Bedrock AgentCore for root cause analysis using 7-day historical sensor baselines
• Three concurrent tasks handle ingestion/inference, MQTT communication with bounded queue, and cloud recommendations
• Component recipe and cross-compilation Dockerfile provided for ARM64 deployment via AWS IoT Greengrass

The pattern keeps latency-sensitive decisions at the edge while routing reasoning-heavy work to the cloud, applicable to predictive maintenance, smart buildings, and fleet telematics.