AWS

• Deploying MCP servers with AWS IoT Greengrass for edge diagnostics

  Deploying Model Context Protocol (MCP) servers as AWS IoT Greengrass components brings autonomous, AI agent-driven network diagnostics to the edge, where connectivity is unreliable and expertise is hard to find. This first post in a series covers why edge diagnostics matter and how MCP maps expert troubleshooting workflows onto an autonomous agent.

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

  Edge AI agents classify sensor anomalies locally on resource-constrained IoT devices and escalate only complex reasoning to the cloud. This post shows how to build a Rust-based AWS IoT Greengrass component that runs quantized ONNX inference on-device and escalates complex anomalies to an Amazon Bedrock AgentCore agent for root cause analysis.

• Automating IoT firmware update orchestration with an AI agent on AWS

  Build an AI agent that autonomously orchestrates IoT firmware deployments. It plans risk-based waves, monitors rollout health, and makes proceed, pause, or rollback decisions through AWS IoT Jobs and Amazon Bedrock.

• Device connectivity in AWS IoT Core: Monitor, diagnose, and act at scale

  Learn how to use the AWS IoT Core connectivity management suite, spanning Fleet Indexing, real-time status and connection introspection APIs, subscription auditing, programmatic disconnect, lifecycle events, and CloudWatch dashboards, to gain complete visibility and control over your IoT device fleet's connectivity without building custom infrastructure.

• Mechanisms for IoT commands, control, and configuration

  AWS IoT Core and AWS IoT Device Management offer several mechanisms to send commands, apply configuration, and control your devices. This post introduces five of them, using a smart bulb as an example, and provides a comparison matrix to help you select the right mechanism for your command, control, and configuration use case.