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

Edge & Lightweight Agents is a practical course focused on deploying agentic AI workloads on resource-constrained devices. Participants learn to build, optimize, and manage lightweight agents capable of local reasoning and inference, improving speed, privacy, and reliability in distributed environments. The course emphasizes performance tuning, low-latency design, and hardware–software integration.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to implement and optimize on-device agentic systems using Python and edge AI frameworks.

By the end of this training, participants will be able to:

Understand the architecture and challenges of running agentic AI on edge devices.
Design lightweight agent loops suitable for constrained environments.
Implement local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
Integrate agents with sensors, actuators, and IoT platforms.
Optimize performance, energy use, and latency for real-time operation.

Format of the Course

Interactive lecture and practical demonstrations.
Hands-on development in local or emulated environments.
Project-based learning and guided implementation exercises.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course is available as onsite live training in Ireland or online live training.

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Course Outline

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

Requirements

Experience with Python programming
Basic understanding of machine learning workflows
Familiarity with embedded or edge computing concepts

Audience

Embedded developers integrating AI into hardware systems
Edge ML engineers designing on-device inference solutions
Robotics teams deploying agentic AI for autonomous operation

21 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.