Online or onsite, instructor-led live AI Agents training courses demonstrate through interactive hands-on practice how to use AI agents to automate tasks, make intelligent decisions, and interact with users in diverse applications.
AI Agents training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live AI Agents training can be carried out locally on customer premises in Richmond Hill or in NobleProg corporate training centers in Richmond Hill.
NobleProg -- Your Local Training Provider
Richmond Hill - The Business Exchange
9225 Leslie Street, Richmond Hill, Canada, L4B 3H6
Place your business in the peaceful surroundings of Richmond Hill, home to leading global brands. Located in the northern suburbs, our The Business Exchange workspace is just a 30-minute drive from central Toronto and the International Airport.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level AI engineers, robotics developers, and automation specialists who wish to leverage DeepSeek for building intelligent AI agents and autonomous systems.
By the end of this training, participants will be able to:
Understand the architecture and capabilities of DeepSeek AI models.
Integrate DeepSeek into AI agents for decision-making and automation.
Apply reinforcement learning techniques for training autonomous systems.
Deploy AI-driven autonomous agents in real-world environments.
Autonomous AI agents are rapidly being integrated into decision-making processes across sectors such as defense, healthcare, finance, and infrastructure. As these systems gain greater independence and autonomy, there is an urgent need for robust ethical frameworks, governance mechanisms, and regulatory oversight to guide their design and deployment.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to explore the ethical, societal, and regulatory implications of deploying autonomous AI agents in real-world environments.
By the end of this training, participants will be able to:
Analyze the ethical risks and moral dilemmas posed by autonomous AI agents.
Evaluate governance frameworks and regulatory models relevant to high-stakes AI deployment.
Assess accountability, explainability, and transparency mechanisms in autonomous systems.
Develop strategies for aligning AI agent behavior with legal, ethical, and societal values.
Format of the Course
Interactive lecture and discussion.
Case studies and role-playing simulations.
Hands-on analysis of governance models and ethical frameworks.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
AI-powered automation combines the flexibility of no-code platforms with the intelligence of large language models and autonomous agents to create scalable, intelligent systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to design, build, and optimize automation pipelines enhanced with AI decision-making, orchestration, and self-execution capabilities.
By the end of this training, participants will be able to:
Design no-code automation workflows enhanced by AI interpretation and responses.
Use prompt chaining, webhook triggers, and APIs to build scalable logic flows.
Create intelligent apps that connect services and interpret data autonomously.
Understand, design, and deploy AI agents capable of goal-oriented action planning and execution.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level AI developers, security specialists, and compliance officers who wish to design and implement secure AI agents while addressing ethical concerns and robustness.
By the end of this training, participants will be able to:
Understand the security risks and ethical challenges in AI agent development.
Implement security-first design principles for AI models.
Apply adversarial robustness techniques to prevent attacks on AI agents.
Ensure compliance with ethical AI guidelines and regulatory standards.
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 instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level to advanced-level AI developers, researchers, and multimedia engineers who wish to build AI agents capable of understanding and generating multi-modal content.
By the end of this training, participants will be able to:
Develop AI agents that process and integrate text, image, and speech data.
Implement multi-modal models such as GPT-4 Vision and Whisper ASR.
Optimize multi-modal AI pipelines for efficiency and accuracy.
Deploy multi-modal AI agents in real-world applications.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level to intermediate-level developers and designers who wish to understand and build AI-powered personal assistants for various use cases.
By the end of this training, participants will be able to:
Understand the core principles of AI-powered personal assistants.
Learn the fundamentals of integrating language models with smart assistants.
Build and customize a personal assistant using AI frameworks and tools.
Deploy personal assistants for real-world applications in different industries.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level AI engineers, enterprise software developers, and business leaders who wish to customize and deploy LLM-based AI agents for enterprise applications.
By the end of this training, participants will be able to:
Understand the architecture and capabilities of open-source LLMs.
Customize and fine-tune LLMs for enterprise use cases.
Deploy AI agents using LangChain and Hugging Face.
Integrate LLM-powered agents into business workflows.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level robotics engineers, AI researchers, and automation specialists who wish to develop AI-driven autonomous robotic systems for complex tasks and human-AI collaboration.
By the end of this training, participants will be able to:
Understand the role of AI agents in robotic decision-making and automation.
Implement AI-driven navigation and obstacle avoidance.
Develop human-AI collaborative robotic systems.
Deploy AI-powered perception and control systems in robots.
This course explores the design, coordination, and implementation of multi-agent systems (MAS) using Python. Participants will learn how to build agents that communicate, collaborate, and adapt to achieve shared objectives in complex, dynamic environments.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design and implement multi-agent systems for intelligent automation, simulation, and decision-making applications.
By the end of this training, participants will be able to:
Understand the architecture and principles of multi-agent systems.
Develop agents capable of communication, coordination, and negotiation.
Implement distributed environments for agent interactions.
Apply reinforcement learning and planning in multi-agent contexts.
Simulate cooperative and competitive agent behaviors.
Design hybrid workflows combining humans and intelligent agents.
Format of the Course
Instructor-led lectures and live demonstrations.
Hands-on exercises using open-source agent frameworks.
Applied group project simulating a multi-agent scenario.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level financial professionals, risk analysts, and AI engineers who wish to develop and deploy AI-driven solutions for financial automation and fraud detection.
By the end of this training, participants will be able to:
Understand the role of AI in financial automation and fraud detection.
Build AI models for detecting fraudulent transactions.
Leverage machine learning for real-time risk assessment.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level to intermediate-level customer service professionals, chatbot developers, and automation teams who wish to implement AI-driven solutions for customer support and service automation.
By the end of this training, participants will be able to:
Understand the fundamentals of AI-powered customer support.
Develop and integrate AI chatbots for handling customer queries.
Leverage NLP and machine learning to enhance chatbot interactions.
Deploy AI agents in real-world customer service applications.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals and AI developers who wish to implement AI-driven healthcare solutions.
By the end of this training, participants will be able to:
Understand the role of AI agents in healthcare and diagnostics.
Develop AI models for medical image analysis and predictive diagnostics.
Integrate AI with electronic health records (EHR) and clinical workflows.
Ensure compliance with healthcare regulations and ethical AI practices.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level to intermediate-level AI developers and UI/UX designers who wish to learn how to build, optimize, and deploy AI-powered virtual assistants.
By the end of this training, participants will be able to:
Design effective conversational AI workflows and UI/UX interfaces.
Implement AI-driven natural language processing (NLP) models.
Integrate AI assistants with APIs and third-party platforms.
Deploy and maintain AI assistants for real-world applications.
Mistral AI is a powerful family of open-source and enterprise-ready AI models for language, multimodal, and agentic applications.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to build, deploy, and manage AI agents using Mistral’s Medium 3, Le Chat Enterprise, and Devstral models.
By the end of this training, participants will be able to:
Understand the architecture and capabilities of Mistral Medium 3, Le Chat Enterprise, and Devstral.
Design and implement AI agents leveraging Mistral models for enterprise and developer use cases.
Integrate coding systems, connectors, and enterprise data into agent workflows.
Optimize performance, cost, and compliance for Mistral-powered agents.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Python is the core language powering the development and orchestration of autonomous AI agents. This course focuses on practical implementation using modern SDKs and frameworks such as LangChain and AutoGen to build, connect, and manage agent workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level backend engineers, platform engineers, and ML engineers who wish to implement and orchestrate autonomous agents using Python tooling and APIs.
By the end of this training, participants will be able to:
Set up and configure Python-based environments for agentic systems.
Use popular agent SDKs like LangChain and AutoGen to create functional agents.
Integrate tools and APIs to extend agent capabilities.
Orchestrate multi-agent workflows and communication patterns.
Apply best practices for debugging, testing, and maintaining agentic codebases.
Format of the Course
Interactive lecture and discussion.
Hands-on programming exercises and live demos.
Practical projects building end-to-end agent workflows.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Google Antigravity is an agentic development environment designed to build autonomous agents capable of planning, reasoning, coding, and acting through Gemini 3’s multimodal capabilities.
This instructor-led, live training (online or onsite) is aimed at advanced-level technical professionals who wish to design, build, and deploy autonomous agents using Gemini 3 and the Antigravity environment.
Upon finishing this training, participants will be prepared to:
Build autonomous workflows that use Gemini 3 for reasoning, planning, and execution.
Develop agents in Antigravity that can analyze tasks, write code, and interact with tools.
Integrate Gemini-driven agents with enterprise systems and APIs.
Optimize agent behavior, safety, and reliability in complex environments.
Format of the Course
Expert demonstrations combined with interactive discussions.
Hands-on experimentation with autonomous agent development.
Practical implementation using Antigravity, Gemini 3, and supporting cloud tools.
Course Customization Options
If your team requires domain-specific agent behaviors or custom integrations, please contact us to tailor the program.
This course teaches practical engineering techniques to design, build, test, and deploy agentic (autonomous) systems using Python. It covers the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, AI developers, and software engineers who wish to build robust, production-ready autonomous agents using Python.
By the end of this training, participants will be able to:
Design and implement the agent loop and decision-making workflows.
Integrate external tools and APIs to extend agent capabilities.
Implement short-term and long-term memory architectures for agents.
Coordinate multi-step orchestrations and agent composability.
Apply safety, access control, and observability best practices for deployed agents.
Format of the Course
Interactive lecture and discussion.
Hands-on labs building agents with Python and popular SDKs.
Project-based exercises that produce deployable prototypes.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level professionals who wish to master the techniques for deploying and managing AI agents in production environments.
By the end of this training, participants will be able to:
Design and implement scalable AI deployment pipelines.
Use tools like Docker and Kubernetes to containerize and orchestrate AI agents.
Monitor and optimize AI agents in production environments.
Implement CI/CD workflows for AI agent deployments.
Ensure compliance with security and data governance requirements.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level AI professionals who wish to master reinforcement learning techniques and implement them for training AI agents in solving complex problems.
By the end of this training, participants will be able to:
Understand the core principles of reinforcement learning and Markov Decision Processes (MDPs).
Design and implement RL algorithms such as Q-Learning, SARSA, and Deep Q-Networks (DQN).
Utilize frameworks like OpenAI Gym and RL libraries for practical applications.
Train AI agents to solve real-world, multi-step decision-making problems.
Address challenges such as exploration-exploitation trade-offs and convergence in RL training.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level AI developers and automation specialists who wish to integrate agentic capabilities into AI-powered applications.
By the end of this training, participants will be able to:
Understand the principles of agentic AI and autonomous decision-making.
Implement goal-driven AI agents with self-optimization techniques.
Integrate multi-agent collaboration for complex problem-solving.
Enhance AI-human interaction through adaptive user experiences.
Deploy agentic AI models in real-world applications.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level AI professionals who wish to master the skills to design, build, and deploy MAS that solve complex, real-world problems.
By the end of this training, participants will be able to:
Understand the principles of multi-agent system architectures.
Implement strategies for communication, coordination, and decision-making in MAS.
Apply game theory to model agent interactions and resolve conflicts.
Leverage frameworks like JADE to create scalable MAS solutions.
Address challenges like scalability, trust, and emergent behavior in MAS.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level professionals who wish to develop and optimize multi-agent systems using Agentic AI frameworks.
By the end of this training, participants will be able to:
Understand the principles of Agentic AI in multi-agent environments.
Develop AI-driven agents that interact autonomously.
Implement reinforcement learning for adaptive AI behavior.
Optimize multi-agent collaboration and competition.
Apply Agentic AI in robotics, gaming, and enterprise automation.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level business analysts, RPA developers, and AI professionals who wish to harness the power of AI agents for business process automation.
By the end of this training, participants will be able to:
Understand the role of AI agents in business process automation.
Identify opportunities for automation in key business domains.
Develop AI-driven solutions for customer support, finance, and supply chain workflows.
Integrate AI agents with existing RPA and business systems.
Evaluate and optimize AI automation strategies for efficiency.
Vertex AI Agent Builder is a no-code/low-code environment for creating grounded agents that combine generative models with retrieval-augmented generation (RAG), allowing teams to rapidly build agents that use enterprise data and search to provide accurate, context-aware responses.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to design, configure, and deploy smart agents using Vertex AI Agent Builder and RAG patterns.
By the end of this training, participants will be able to:
Design grounded agent workflows using Agent Builder.
Implement RAG pipelines with search and vector stores.
Integrate enterprise data sources securely for retrieval.
Evaluate and iterate agent behavior using testing and metrics.
Format of the Course
Interactive lecture and discussion.
Hands-on labs using Vertex AI Agent Builder and RAG components.
Project-based exercises to build and refine agents.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level game developers and AI enthusiasts who wish to integrate AI agents into gaming applications effectively.
By the end of this training, participants will be able to:
Understand the role of AI agents in modern gaming.
Develop decision-making systems using decision trees and finite state machines.
Implement pathfinding algorithms such as A* for in-game navigation.
Apply reinforcement learning techniques to create adaptive AI behaviors.
Optimize AI performance for real-time gaming environments.
LLMs and autonomous agent frameworks like AutoGen and CrewAI are redefining how DevOps teams automate tasks such as change tracking, test generation, and alert triage by simulating human-like collaboration and decision-making.
This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to design and implement DevOps automation workflows powered by large language models (LLMs) and multi-agent systems.
By the end of this training, participants will be able to:
Integrate LLM-based agents into CI/CD workflows for smart automation.
Automate test generation, commit analysis, and change summaries using agents.
Coordinate multiple agents for triaging alerts, generating responses, and providing DevOps recommendations.
Build secure and maintainable agent-powered workflows using open-source frameworks.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at advanced-level professionals who wish to leverage Agentic AI for decision-making in complex business and technical scenarios.
By the end of this training, participants will be able to:
Understand the principles of autonomous decision-making in AI.
Design and implement AI agents that operate with minimal human intervention.
Integrate Agentic AI into automation workflows and business systems.
Optimize AI-driven decision processes for efficiency and scalability.
Ensure compliance, security, and ethical considerations in AI autonomy.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level professionals who wish to delve deeper into the design and development of autonomous agents for practical applications.
By the end of this training, participants will be able to:
Understand the foundational concepts of autonomous agents.
Explore real-world applications of autonomous AI agents.
Design, train, and implement agents using reinforcement learning.
Integrate agents into existing systems for automation and decision-making.
Address ethical considerations and challenges in deploying autonomous agents.
Devstral is an open-source framework designed for building and running coding agents that can interact with codebases, developer tools, and APIs to enhance engineering productivity.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, developer-tooling teams, and SREs who wish to design, implement, and optimize coding agents using Devstral.
By the end of this training, participants will be able to:
Set up and configure Devstral for coding agent development.
Design agentic workflows for codebase exploration and modification.
Integrate coding agents with developer tools and APIs.
Implement best practices for secure and efficient agent deployment.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level professionals who wish to understand how AI agents work and build their own interactive chatbot systems.
By the end of this training, participants will be able to:
Understand the basics of AI agents and their applications.
Explore the principles of natural language processing (NLP).
Design a conversational flow for a chatbot.
Develop a functional chatbot using Python and related tools.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level professionals who wish to understand and create simple AI agents for real-world applications.
By the end of this training, participants will be able to:
Understand the fundamental concepts of AI agents.
Identify different types of AI agents and their applications.
Design and implement basic AI agents for practical tasks.
Explore tools and frameworks for building AI agents.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level / intermediate-level developers and cloud practitioners who wish to use Alibaba Cloud to build AI agents that can automate tasks, answer questions, and connect with business systems.
By the end of this training, participants will be able to: understand AI agent architecture on Alibaba Cloud, build a simple agent workflow, connect an agent to enterprise knowledge and tools, deploy and monitor an agent in a cloud environment.
This advanced practical seminar is designed for software engineers, architects, technical leads, engineering managers, and delivery teams who want to understand how Artificial Intelligence can accelerate the full software delivery lifecycle, from requirements analysis to production-ready code.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level developers and technical professionals who wish to use Microsoft Azure to build, test, and deploy AI agents for business applications.
By the end of this training, participants will be able to: understand AI agent architecture on Azure, create and configure a working agent, connect agents to business knowledge sources, evaluate and prepare agents for deployment.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at software developers and DevOps teams who wish to use OpenAI Codex CLI to automate coding tasks, review code, and run multi-step workflows from the terminal.
By the end of this training, participants will be able to install and configure Codex CLI, execute tasks via natural language prompts, manage approval modes, and integrate with Git and CI pipelines.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers who wish to use GitHub Copilot Agent Mode to build features autonomously, run tests, and manage larger coding tasks.
By the end of this training, participants will be able to activate Agent Mode, plan and iterate within the agent loop, execute terminal commands, and implement enterprise governance.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers and leads who wish to use Cursor to build features faster, refactor across files, and manage team-wide AI workflows.
By the end of this training, participants will be able to navigate using Composer and agent mode, target code with context-aware chat, deploy team workspaces, and integrate Cursor with version control.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level solution architects, AI engineers, developers, and technical product teams who wish to use Tencent ADP to build enterprise AI agents with RAG, workflow automation, and operational guardrails.
By the end of this training, participants will be able to: design ADP-based agents, create knowledge pipelines, orchestrate workflows and multi-agent patterns, and apply production guardrails.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level developers and technical practitioners who wish to use Google Cloud to build AI agents that can automate tasks, use tools, and work with business data.
By the end of this training, participants will be able to: explain AI agent concepts, build agents with Vertex AI, connect agents to external tools and data, and deploy agents for business use cases.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers and DevOps professionals who wish to use Hermes Agent to automate development tasks, manage infrastructure, and build persistent AI workflows.
By the end of this training, participants will be able to install and configure Hermes Agent, interact with it across multiple platforms, use 40+ built-in tools, and deploy on various backends.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at business users, operations teams, and technical professionals who wish to use Hermes Agent to automate work, create reusable AI-driven processes, and improve team productivity.
By the end of this training, participants will be able to: explain Hermes Agent basics, set up and use core features, build practical workflows, and apply good operational practices.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers who wish to use Hermes Agent to design and run secure local AI workflows for code assistance.
By the end of this training, participants will be able to: explain Hermes Agent concepts, set up secure local agent environments, build practical coding workflows, and apply safety and governance controls.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers and technical teams who wish to use Ollama and open models to build and run private agentic AI solutions on internal infrastructure.
By the end of this training, participants will be able to: install and configure Ollama, evaluate and run open models locally, create simple agentic and retrieval-based workflows, and apply security and governance controls for regulated environments.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at backend developers who wish to build MCP-compatible tool servers and integrate them with agent clients.
By the end of this training, participants will be able to implement MCP servers, connect them to Claude Code and OpenClaude, switch transports, and maintain an internal tool registry.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level AI professionals who wish to use MCP to connect AI assistants with external tools, data, and enterprise services.
By the end of this training, participants will be able to: explain MCP concepts, identify key architecture components, set up a basic integration, and apply security good practices.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at beginner-level to intermediate-level developers and technical teams who wish to use OpenAI APIs to build AI agents that can reason, use tools, and complete multi-step tasks.
By the end of this training, participants will be able to: design agent workflows, connect tools and external data, manage context and responses, and test agents for business use.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at intermediate-level / advanced-level developers and technical professionals who wish to use OpenClaw to build and run practical AI agents that automate work through chat and tool integrations.
By the end of this training, participants will be able to: install and configure OpenClaw, connect an LLM provider, use and adapt skills for automation, and apply basic security and operational guardrails.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at senior developers who wish to use Pi to build lightweight, customizable terminal agent workflows.
By the end of this training, participants will be able to install Pi, extend it with TypeScript skills, adapt workflows to team standards, and share extensions via npm.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers who wish to use Pi Coding Agent to generate code, review changes, and streamline terminal-based development workflows.
By the end of this training, participants will be able to: explain Pi Coding Agent concepts, set up and use the tool effectively, apply it to practical coding tasks, and adopt safe team workflows.
This instructor-led, live training in Richmond Hill (online or onsite) is aimed at developers who wish to use Windsurf to build features conversationally, execute terminal workflows inside the IDE, and refactor across the codebase.
By the end of this training, participants will be able to use Cascade for navigation, execute terminal commands inside the editor, leverage predictive edits, and customize Windsurf for proprietary stacks.
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Testimonials (6)
the agent and SMA code path
Sylvie Varesano - Aubay
Course - Developing Multi-Agent Systems
Machine Translated
It triggered awareness about working with complex topics and the way, how to start.
Victoria Schmid - Medeo Investment AG
Course - Getting Started with Manus AI
The mix of theory and practice and of high level and low level perspectives
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Autonomous Decision-Making with Agentic AI
practical exercises
Daniel - Facultatea S.A.I.A.P.M.
Course - Agentic AI in Multi-Agent Systems
I liked that he constantly provided examples but also offered time for individual work on what he presented.
Iacob Giorgel
Course - Agentic AI Unleashed: Crafting LLM Applications with AutoGen
The content, the interactions, and working on topics that were relevant to our daily lives
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