AI Agents for Robotics and Human-AI Collaboration Training Course

AutoGen is an open-source framework from Microsoft for building multi-agent applications that use LLMs, tools, memory, and user interaction.

This instructor-led, live training (online or onsite) is aimed at advanced-level developers and architects who wish to design and deploy deeply customized agents using AutoGen’s Python-based APIs, function-calling capabilities, and modular toolchains.

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

• Develop custom agents with role-specific logic and tool routing.
• Build dynamic workflows using advanced function calling and context switching.
• Implement memory modules and planning frameworks within agent teams.
• Handle multi-agent error states and adaptive retry mechanisms.

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 New Zealand (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to integrate Read AI with platforms like Slack, CRM systems, and Notion to automate workflows and improve team efficiency.

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

• Connect Read AI with Slack, Salesforce, Notion, and similar tool.
• Automate the delivery of meeting summaries and action items across platforms.
• Sync Read AI data with CRM systems and task boards.
• Troubleshoot integration issues and optimize configurations for team needs.

AutoGen for Enterprise AI Automation is a hands-on course focused on implementing scalable, intelligent agent systems to automate complex business operations using the AutoGen framework.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI professionals who wish to deploy multi-agent architectures across enterprise platforms and processes using the AutoGen framework.

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

• Design and automate enterprise workflows using AutoGen and LLM agents.
• Integrate AutoGen with LangChain for advanced orchestration and context handling.
• Build RAG pipelines and connect enterprise data for contextual automation.
• Connect agents with enterprise platforms like Slack, Jira, and SharePoint.
• Scale and monitor AutoGen deployments in production environments.

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 New Zealand (online or onsite) is aimed at intermediate-level business and AI professionals who wish to create intelligent, domain-specific business agents using CrewAI.

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

• Understand the architecture of CrewAI and its relevance in business use cases.
• Create business-oriented agents using roles, tools, and memory.
• Build agent crews that collaborate to perform business workflows.
• Apply CrewAI in practical scenarios such as finance, marketing, and customer support.

This instructor-led, live training in New Zealand (online or onsite) is aimed at beginner-level professionals who wish to explore the fundamentals of CrewAI and build simple multi-agent systems.

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

• Understand the architecture and design principles of CrewAI.
• Define roles, tasks, and flows within a crew of agents.
• Create collaborative workflows using CrewAI's framework.
• Build, test, and run basic multi-agent scenarios.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to scale CrewAI systems, integrate with enterprise tools, and deploy automation solutions in production environments.

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

• Design scalable multi-agent systems using CrewAI.
• Integrate agents with enterprise tools like Slack, databases, and APIs.
• Implement monitoring, logging, and diagnostics for agent behavior.
• Deploy, manage, and scale CrewAI solutions in production environments.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level professionals who wish to automate business and technical workflows using CrewAI through real-world use cases and tool integrations.

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

• Understand the architecture and core principles of CrewAI.
• Design workflows involving multiple collaborating agents.
• Integrate CrewAI with APIs, tools, and external systems.
• Implement and orchestrate real-world automation use cases.

This instructor-led, live training in New Zealand (online or onsite) is aimed at advanced-level professionals who wish to design and implement custom multi-agent systems using CrewAI with complex workflows, event triggers, and tool integrations.

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

• Design and build custom AI agents with specialized roles and tools.
• Implement complex, event-driven multi-agent task flows.
• Integrate external APIs and data pipelines within a CrewAI system.
• Optimize coordination, error handling, and execution efficiency of multi-agent systems.

AutoGen Studio is a visual environment for creating and managing LLM-based multi-agent workflows without requiring code.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level business and innovation professionals who wish to use AutoGen Studio to visually design, test, and refine agent interactions for internal automation or AI-enhanced product development.

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

• Create multi-agent workflows using a no-code interface.
• Define agent roles, prompts, and goals using AutoGen Studio.
• Visualize and manage message flows between agents.
• Incorporate error handling and context refinement into agent logic.

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 New Zealand (online or onsite) is aimed at beginner-level professionals who wish to understand the capabilities, use cases, and potential applications of Grok AI.

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

• Understand what Grok AI is and how it differs from other chatbots.
• Explore the key features and functionalities of Grok AI.
• Interact effectively with Grok AI for personal and business use.
• Leverage Grok AI for productivity, creativity, and problem-solving.
• Recognize the ethical considerations and limitations of AI chatbots.

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 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.

This instructor-led, live training in New Zealand (online or onsite) is aimed at beginner-level professionals who wish to learn how to use Read AI to capture meeting summaries, extract key insights, and generate action items with minimal manual effort.

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

• Set up and configure Read AI for meetings across major platforms.
• Automatically generate meeting summaries and identify action items.
• Interpret engagement and sentiment analytics provided by Read AI.
• Share, edit, and organize summaries effectively for team collaboration.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level professionals who wish to streamline remote team collaboration using AI-powered workflows and Read AI analytics.

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

• Design complete remote team meeting workflows using Read AI.
• Automate follow-ups and documentation to reduce meeting overhead.
• Leverage AI summaries for both synchronous and asynchronous collaboration.
• Track team engagement and accountability through Read AI insights.

This instructor-led, live training in New Zealand (online or onsite) is aimed at advanced-level professionals who wish to build secure and compliant agent workflows using CrewAI in enterprise environments.

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

• Design secure and auditable workflows involving multiple agents.
• Implement data privacy strategies within autonomous systems.
• Integrate logging, governance, and compliance mechanisms.
• Deploy and monitor secure CrewAI-based systems in production environments.