This course explains how multiple AI agents can communicate, collaborate, and solve complex tasks through structured conversations.
Overview
AutoGen Agentic AI Training introduces participants to building and managing agentic AI systems using the AutoGen framework. This course explains how multiple AI agents can communicate, collaborate, and solve complex tasks through structured conversations. Participants will learn how AutoGen enables scalable, controllable, and goal-driven agent interactions for business and technical use cases.
Learning Outcomes
- Understand AutoGen and agentic AI concepts
- Build multi-agent AI workflows and systems
- Automate tasks using AI agents
- Integrate AutoGen with applications and APIs
- Evaluate and optimize agent performance
Duration & Delivery Mode
14 hours
Target Audience
• AI and automation professionals
• Product managers and solution architects
• Business analysts and innovation teams
• Developers and technical consultants
• Professionals exploring agentic AI systems
Pre-requisites
• Basic understanding of artificial intelligence concepts
• Familiarity with workflows or problem-solving processes
• No advanced programming background required
Skillset Achieved
• Understanding AutoGen agent architecture
• Designing conversational AI agents
• Managing multi-agent interactions and workflows
• Applying AutoGen to real-world use cases
• Implementing responsible and governed agent systems
Course Outcome
By the end of this training, participants will be able to design, deploy, and manage agentic AI systems using AutoGen. Learners will gain practical skills to create collaborative AI agents that communicate effectively while maintaining oversight, accuracy, and responsible AI practices.
Course Outline
Introduction to AutoGen and Agentic AI
• What is AutoGen and how it works
• Agentic AI concepts and terminology
• Key differences between AutoGen and other agent frameworks
AutoGen Architecture and Core Components
• Agents, roles, and conversation flows
• Task coordination through conversations
• Tools, memory, and context handling
Designing Single and Multi-Agent Conversations
• Defining agent roles and responsibilities
• Structuring effective agent dialogues
• Managing agent decision-making
Basic AutoGen Use Cases
• Research and analysis agents
• Planning and problem-solving workflows
• Content and knowledge generation tasks
Advanced Agent Collaboration Patterns
• Multi-agent coordination strategies
• Iterative reasoning and feedback loops
• Managing agent conflicts and redundancy
Business and Enterprise Applications
• AutoGen for business process support
• Cross-functional AI agent collaboration
• Decision support and advisory systems
Governance, Ethics, and Risk Management
• Data privacy and security considerations
• Bias, accuracy, and reliability challenges
• Responsible deployment of agentic AI
Hands-on Practice and Demonstrations
• Live AutoGen agent conversations
• Real-world scenario-based exercises
• Participant practice and feedback
Assessment Topics
- Introduction to AutoGen
- Agentic AI workflow fundamentals
- Multi-agent communication and orchestration
- API integration and automation
- AI agent evaluation and optimization
Evaluation
• Participation in hands-on agent exercises
• Practical agent design assignment
• Knowledge-based assessment
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in AutoGen Agentic AI Training, validating their ability to design and manage agent-based AI systems.
Enroll Now
Other cities in Malaysia
Explore the same course in other cities across Malaysia.
Cities across the globe for this course
This course also runs in these cities in other countries.
UK Classrooms
US Classrooms
Countries where this course is available
Browse all the countries currently offering scheduled delivery for this course.
What Our Students Say
“Excellent introduction to agentic AI using AutoGen.”
“The conversational agent approach was very well explained.”
“Great hands-on sessions with real AutoGen scenarios.”
“Helped us understand how to control multi-agent AI systems.”
“A solid foundation for anyone working with agentic AI.”