Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Learn to deploy small language models on resource-constrained edge devices through this 19-minute conference talk that bridges strategy and hardware implementation. Discover how to build multi-agent systems that operate effectively on tiny boards and unreliable networks, starting with governance fundamentals including mission clarity, observability, auditability, and drift monitoring to ensure actions align with intended outcomes. Explore the anatomy of edge agents covering sensor inputs, actuator outputs, planning layers, and the strategic choice between on-device small models and cloud-based large language models, with hybrid patterns providing low-latency local control and deeper reasoning when connectivity permits. Examine scalable architecture featuring agent-specific runtimes for different device classes, storage models balancing edge privacy with cloud capabilities, and open multi-model strategies matching tasks to appropriate inference footprints. Understand security and compliance through operational standards that detect drift and maintain decision audit trails, while exploring interoperability through emerging standards like MCP for unified tool and data access, and A2A for agent coordination through shared missions and capabilities. Review four field-tested patterns including single specialized agents, embedded third-party agents, multi-agent orchestration, and federated networks with guidance on when to apply each approach. See practical implementation through a demonstration of FaustHub.ai, a no-code visual builder that compiles to C for Arduino and ESP32 platforms, complete with device registry, governance, and communication layers that transforms drag-and-drop blocks into running firmware.
