Small Language Models on Resource-Constrained Platforms
Edge AI gets real when code meets copper. We take you from strategy to hardware with a clear blueprint for building multi-agent systems that actually run on tiny boards and messy networks. We start by grounding the work in governance: mission clarity, observability, auditability, and drift monitoring so actions stay aligned with intent. From there, we break down the anatomy of an edge agent—sensor inputs, actuator outputs, the planning layer, and the choice between small on-device models and cloud LLMs. Hybrid patterns shine, giving you low-latency control locally and deeper reasoning when connectivity allows.
We also dig into architecture that scales: an agent-specific runtime on each device class, storage models that balance edge privacy with cloud reach, and an open, multi-model strategy to match tasks with the right inference footprint. Security and compliance stay front and center, with operational standards that flag drift and preserve a trail of why a decision was made. Interoperability is where everything clicks, so we highlight two emerging standards. MCP unifies access to tools, APIs, and data sources, while A2A enables agents to share missions, capabilities, and context through agent cards. Together they let a home energy optimizer coordinate with a pump agent or a solar agent without hardcoding brittle integrations.
You’ll hear four patterns we see in the field—single specialized agents, embedded third-party agents, multi-agent orchestration, and federated networks—and when to use each. To make it concrete, we demo FaustHub.ai: a no-code visual builder that compiles to C for Arduino and ESP32, complete with a device registry, governance, and communication layers. Watching an agent go from drag-and-drop blocks to running firmware makes the concepts tangible and shows how fast teams can move once standards and guardrails are in place.
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