Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

• Core concepts of LLM agents and multi-agent architecture
• Overview of the AutoGen framework and its ecosystem
• Key agent roles: user proxy, assistant, function caller, and others

Installing and Configuring AutoGen

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

Agent Design and Role Assignment

• Exploring agent types and conversation patterns
• Setting agent goals, prompts, and operational instructions
• Role-based task delegation and control flow management

Function Calling and Tool Integration

• Registering functions for agent utilization
• Executing functions autonomously and collaboratively
• Linking external APIs and Python scripts to agents

Conversation Management and Memory

• Session tracking and persistent memory implementation
• Inter-agent messaging and token management
• Controlling conversation context and history

End-to-End Agent Workflows

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

Use Cases and Deployment

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

Summary and Next Steps