Edge & Lightweight Agents: On-Device Agentic Workloads with Python Training Course

Introduction to Edge and Agentic AI

• Overview of agentic AI and edge computing
• Latency, privacy, and bandwidth considerations
• Architectural comparison: cloud vs. edge agents

Designing Lightweight Agent Architectures

• Breaking down the agent loop for constrained systems
• Asynchronous design for efficient computation
• Balancing autonomy and connectivity

Setting Up the Development Environment

• Installing Python frameworks for edge AI
• Configuring TensorFlow Lite and PyTorch Mobile
• Deploying test environments on Raspberry Pi or similar devices

Implementing On-Device Inference

• Converting and quantizing models for edge deployment
• Running inference with TensorFlow Lite and ONNX Runtime
• Integrating inference results into agent decision loops

Integrating Agents with Hardware and IoT

• Connecting sensors, actuators, and IoT modules
• Local data collection and processing pipelines
• Offline operation and event-triggered behavior

Optimization and Monitoring

• Performance tuning for low power and high speed
• Edge caching and model compression techniques
• Monitoring and debugging edge agents

Hands-on Project: Deploying a Lightweight Agent on Edge Hardware

• Designing a small autonomous agent for an IoT or robotics task
• Implementing model inference and local logic
• Testing and optimizing for latency and reliability

Summary and Next Steps