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 Waterloo or in NobleProg corporate training centers in Waterloo.
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This instructor-led, live training in Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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 Waterloo (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.
Google Antigravity is an agent-first development environment designed to streamline engineering workflows through intelligent automation.
This instructor-led, live training (online or onsite) is aimed at beginner-level practitioners who wish to explore the fundamentals of Antigravity and understand how agent-driven coding environments enhance productivity.
Upon completion of this training, participants will be able to:
Install and configure Google Antigravity.
Navigate and understand both the Editor View and Manager View.
Work effectively with agents to automate simple development tasks.
Use Antigravity to generate, refine, and manage project files.
Format of the Course
Instructor explanations supported by real-time demonstrations.
Guided exercises focused on hands-on use of agents.
Practical exploration of core Antigravity features in a controlled lab environment.
Course Customization Options
If you require a tailored version of this training, please contact us to arrange a customized program.
This instructor-led, live training in Waterloo (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.
Amazon Bedrock AgentCore provides the infrastructure to design, orchestrate, and manage multi-agent systems, enabling collaboration, specialization, and advanced automation patterns across multiple AI-driven entities.
This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to design, deploy, and optimize next-generation multi-agent systems using AgentCore’s orchestration and governance features.
By the end of this training, participants will be able to:
Understand the principles and architectures of multi-agent systems.
Design orchestration strategies for agent collaboration.
Implement communication and negotiation protocols between agents.
Deploy scalable, enterprise-ready multi-agent solutions using AgentCore.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with AgentCore orchestration features.
Case studies of enterprise multi-agent applications.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Waterloo (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 Waterloo (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.
Google Antigravity is a platform for building agents capable of interacting with web applications, browser environments, and multi-surface workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to build, automate, and test browser-based workflows using Google Antigravity.
Upon completion of the training, participants will be able to:
Create agents that interact with web applications in a browser surface.
Automate end-to-end workflows across browser contexts.
Validate and troubleshoot agent behavior in UI-driven environments.
Implement cross-surface automation strategies using Antigravity.
Format of the Course
Guided instruction supported by demonstrations.
Practical, hands-on activities and scenario-based exercises.
Implementation of agent workflows in an interactive lab environment.
Course Customization Options
For customized training requirements, please contact us to tailor the course to your objectives.
AgentCore simplifies the process of building, enhancing, and monitoring fully managed AI agents by providing a unified suite of services tailored for deployment at scale.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level practitioners who wish to gain hands-on experience creating production-ready AI agents with AgentCore.
By the end of this training, participants will be able to:
Understand the core capabilities of AgentCore for AI agent development.
Design and configure simple AI agents using managed services.
Integrate workflows to enhance agent functionality.
Deploy and monitor AI agents for production environments.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with AgentCore services.
Guided exercises from agent concept to deployment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mastra is a framework that provides structured tools for evaluating, debugging, and assuring the reliability of AI agents operating across complex workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to rigorously test agent behavior, improve reliability, and implement measurable evaluation processes.
At the end of this training, participants will confidently:
Apply debugging techniques to identify and correct agent behavior issues.
Evaluate agents using structured metrics, benchmarks, and quality scores.
Implement tooling and workflows that track reliability, drift, and hallucinations.
Design QA strategies that ensure consistent and predictable agent performance.
Format of the Course
Interactive lecture and discussion.
Hands-on debugging and evaluation exercises.
Live-lab analysis of agent behaviors using observability tools.
Course Customization Options
Customized reliability testing scenarios and industry-specific QA methods can be arranged upon request.
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.
Mastra is a framework that supports deep integration between AI agents, APIs, enterprise applications, and external data systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers who wish to build reliable, secure, and scalable integrations between Mastra agents and the broader enterprise ecosystem.
Once this training is completed, participants will be prepared to:
Implement API-driven integrations between Mastra agents and external services.
Connect enterprise data systems and tools to automated agent workflows.
Apply secure data exchange and authentication best practices.
Design integration layers that are scalable, maintainable, and production ready.
Format of the Course
Interactive lecture and discussion.
Hands-on integration engineering and API exercises.
Live-lab implementation using real-world enterprise scenarios.
Course Customization Options
Custom API scenarios, enterprise system mappings, or data-integration workshops are available upon request.
AgentCore provides memory persistence, a secure code interpreter, and a browser tool that enable AI agents to deliver interactive, dynamic, and context-aware experiences.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical practitioners who wish to design and deploy AI agents capable of long-term context retention, on-the-fly computation, and direct interaction with web UIs.
By the end of this training, participants will be able to:
Implement AgentCore memory for stateful, context-aware workflows.
Leverage the secure code interpreter for dynamic calculations and transformations.
Integrate the browser tool for real-time data retrieval and UI interaction.
Design interactive agents for analytics, customer support, and research use cases.
Format of the Course
Interactive lecture and discussion.
Hands-on lab exercises with AgentCore memory and tools.
Case studies in analytics, automation, and customer support scenarios.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Waterloo (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.
Antigravity is a framework that represents advanced agent-driven development workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate to advanced professionals who wish to verify, validate, and secure the output produced by AI agents working within Antigravity-driven environments.
Upon completing this training, participants will be able to:
Assess the accuracy and safety of agent-generated code artifacts.
Use structured techniques to verify agent-executed tasks.
Analyze browser recordings and trace agent activity effectively.
Apply QA and security principles to ensure the reliability of agent workflows.
Format of the Course
Instructor-guided technical briefings and discussions.
Practical exercises focused on verifying real agent workflows.
Hands-on testing and validation within a controlled lab environment.
Course Customization Options
Adaptation of scenarios, workflows, and testing examples is available upon request.
This instructor-led, live training in Waterloo (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.
Google Antigravity is an advanced framework for experimentation with long-lived agents and emergent interactive behaviors.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design, analyze, and optimize agents capable of retaining memories, improving through feedback, and evolving over long operational horizons.
Upon completing this course, participants will gain the skills to:
Design long-term memory structures for agent persistence.
Implement effective feedback loops to shape agent behavior.
Evaluate learning trajectories and model drift.
Integrate memory mechanisms into complex multi-agent ecosystems.
Format of the Course
Expert-led discussion paired with technical demonstrations.
Hands-on exploration through structured design challenges.
Application of concepts to simulated agent environments.
Course Customization Options
If your organization requires tailored content or case-specific examples, please contact us to customize this training.
This instructor-led, live training in Waterloo (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.
AgentCore provides built-in identity, observability, and compliance features that enable organizations to deploy AI agents responsibly in enterprise environments.
This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to design and operate secure, auditable, and compliant AI agent systems using Amazon Bedrock AgentCore.
By the end of this training, participants will be able to:
Implement enterprise identity and permissioning models for agents.
Enable observability through structured logging, metrics, and tracing.
Apply compliance controls to align with regulatory frameworks.
Audit agent activity and maintain secure session-level controls.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with AWS security and monitoring tools.
Case studies in regulated enterprise environments.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mastra is a framework that enables sophisticated workflow automation and coordination across multiple AI agents operating within distributed systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who want to design, orchestrate, and operate multi-agent workflows at scale.
By completing this training, participants will gain the skills to:
Design complex workflows using Mastra’s orchestration capabilities.
Coordinate multiple agents performing parallel or dependent tasks.
Implement monitoring and debugging tools for workflow execution.
Optimize orchestration logic for reliability, throughput, and automation efficiency.
Format of the Course
Interactive lecture and discussion.
Hands-on workflow design and automation exercises.
Practical implementation in a containerized live-lab environment.
Course Customization Options
Customized automation scenarios, enterprise integrations, or workflow patterns can be provided upon request.
AgentCore Runtime & Gateway is an AWS service pairing for packaging, deploying, and securely exposing AI agents with streamlined integrations to external systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineering teams who wish to move from agent prototypes to production by mastering the AgentCore Runtime for deployment and the Gateway for secure connectivity and API integration.
By the end of this training, participants will be able to:
Stand up AgentCore Runtime environments and package agents for deployment.
Expose agents through Gateway with authenticated, rate-limited endpoints.
Integrate external tools and APIs into agent workflows using stable contracts.
Instrument observability, logging, and usage monitoring for production operation.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with Runtime deployments and Gateway integrations.
Practical exercises focused on reliability, security, and rollout.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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 Waterloo (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.
Mastra is an operational framework designed to streamline the deployment, scaling, and lifecycle management of AI agents in production environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical professionals who need to operationalize AI agents reliably and efficiently across production systems.
Upon completion of this training, attendees will be equipped to:
Deploy Mastra-based AI agents into controlled, production-grade environments.
Scale agents horizontally and vertically using platform-native primitives.
Implement observability pipelines to track agent behaviour and performance.
Optimize runtime configurations to reduce latency, costs, and operational risks.
Format of the Course
Interactive lecture and discussion.
Hands-on exercises focused on real deployment scenarios.
Live-lab implementation using containerized and orchestrated environments.
Course Customization Options
Customization of topics, hands-on labs, or industry-specific scenarios is available upon request.
Google Antigravity is an agent-centric development platform used to orchestrate, supervise, and coordinate AI-driven coding and automation workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to design, manage, and optimize multi-agent workflows within Google Antigravity.
Upon completion of this training, participants will gain the skills to:
Configure agent responsibilities and orchestration pipelines within the Manager interface.
Generate and interpret Antigravity artifacts, including task lists, plans, logs, and browser recordings.
Implement verification strategies to ensure agent actions remain transparent and auditable.
Optimize multi-agent collaboration for complex development and operational tasks.
Format of the Course
Guided presentations and practical demonstrations.
Scenario-based exercises focused on real workflow challenges.
Hands-on experimentation within a live Antigravity workspace.
Course Customization Options
If you require a tailored version of this course, please contact us to discuss customization options.
This instructor-led, live training in Waterloo (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.
Antigravity is a development platform designed to build AI-driven, agent-first applications.
This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to create real-world applications using autonomous AI agents within the Antigravity environment.
After completing this training, participants will be equipped to:
Develop applications that rely on autonomous and coordinated AI agents.
Use the Antigravity IDE, editor, terminal, and browser for end-to-end development.
Manage multi-agent workflows with the Agent Manager.
Integrate agent capabilities into production-grade software systems.
Format of the Course
Blended presentations with in-depth demonstrations.
Extensive hands-on practice and guided exercises.
Real implementation work inside the Antigravity live environment.
Course Customization Options
For tailored content aligned with your development stack, please contact us to arrange a customized version of this training.
Amazon Bedrock AgentCore is an enterprise-ready framework for building, deploying, and scaling AI agents with integrated support for memory, observability, and secure identity management.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level engineers and architects who wish to design, secure, and operate agentic AI systems using AWS Bedrock AgentCore.
By the end of this training, participants will be able to:
Understand the architecture and components of AgentCore.
Deploy and manage AI agents with Runtime and Gateway.
Implement persistent memory and stateful interactions.
Apply identity, observability, and compliance controls.
Design multi-agent systems for enterprise-scale workflows.
Format of the Course
Interactive lecture and discussion.
Hands-on AWS lab sessions with AgentCore.
Practical exercises with deployment and monitoring scenarios.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Building LLM Agent Systems with AutoGen is a hands-on course focused on developing multi-agent systems using Microsoft’s AutoGen framework for large language models (LLMs).
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI and automation professionals who wish to design, implement, and orchestrate multi-agent systems using AutoGen with Python and LLMs.
By the end of this training, participants will be able to:
Design multi-agent architectures using the AutoGen framework.
Configure agent roles, capabilities, and coordination behaviors.
Use function-calling and memory handling for agent interactions.
Build and test Python-based LLM agent workflows for real use cases.
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 (online or onsite) is aimed at intermediate-level software developers and engineering teams who wish to build scalable, observable AI systems using Mastra.
By the end of this training, participants will be able to:
Understand Mastra’s architecture and how it integrates with LLMs and external APIs.
Design and implement AI agents and workflows using TypeScript.
Use Mastra’s observability and memory tools to monitor and improve agent performance.
Deploy production-ready AI applications leveraging Mastra’s framework features.
This 1-day workshop, designed for developers, data scientists, and AI enthusiasts, will help you understand and harness the power of agentic AI systems using AutoGen v0.4.
Through a mix of hands-on exercises and practical demonstrations, you’ll learn how to build, manage, and deploy multi-agent applications powered by Large Language Models (LLMs).
By the end of the course, you'll gain a solid foundation in AutoGen’s layered architecture, master asynchronous communication between agents, and explore real-world use cases and best practices for developing scalable and intelligent LLM-driven applications.
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Testimonials (5)
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
Trainer responding to questions on the fly.
Adrian
Course - Agentic AI Unleashed: Crafting LLM Applications with AutoGen
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