Building LLM Agent Systems with AutoGen Training Course

Foundations of LLM Agent Systems

• Concepts of LLM agents and multi-agent architectures
• Introduction to the AutoGen framework and ecosystem
• Agent roles: user proxy, assistant, function caller, and others

Setting Up and Configuring AutoGen

• Configuring the Python environment and dependencies
• Basics of AutoGen configuration files
• Integrating with LLM providers (OpenAI, Azure, local models)

Agent Architecture and Role Definition

• Exploring agent types and interaction patterns
• Specifying agent objectives, prompts, and directives
• Role-based task distribution and control logic

Function Invocation and Tool Integration

• Registering functions for agent utilization
• Independent and cooperative function execution
• Linking external APIs and Python scripts to agents

Conversation Control and Memory Management

• Session tracking and persistent memory storage
• Inter-agent messaging and token management
• Oversight of conversation context and history

Complete Agent Workflow Development

• Constructing multi-step collaborative tasks (e.g., document analysis, code review)
• Modeling user-agent dialogues and decision paths
• Debugging and optimizing agent performance

Applications and Deployment

• Internal automation agents: research, reporting, scripting
• External-facing bots: chat assistants, voice integrations
• Encapsulating and deploying agent systems in production environments

Recap and Future Directions