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.

• 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

• AI and automation professionals
• Product managers and solution architects
• Business analysts and innovation teams
• Developers and technical consultants
• Professionals exploring agentic AI systems

• Basic understanding of artificial intelligence concepts
• Familiarity with workflows or problem-solving processes
• No advanced programming background required

• 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

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.

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

• Introduction to AutoGen
• Agentic AI workflow fundamentals
• Multi-agent communication and orchestration
• API integration and automation
• AI agent evaluation and optimization

• Participation in hands-on agent exercises
• Practical agent design assignment
• Knowledge-based assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

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.