Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

• Concepts of LLM agents and multi-agent architectures
• Overview of the AutoGen framework and its ecosystem
• Defining 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
• Integration with LLM providers (OpenAI, Azure, and local models)

Agent Design and Role Assignment

• Analyzing agent types and conversation dynamics
• Specifying agent objectives, prompts, and operational instructions
• Implementing role-based task delegation and control flow

Function Calling and Tool Integration

• Registering functions for agent utilization
• Executing autonomous and collaborative functions
• Linking external APIs and Python scripts to agents

Conversation Management and Memory

• Tracking sessions and maintaining persistent memory
• Handling agent-to-agent messaging and token management
• Managing conversation context and historical data

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 for research, reporting, and scripting
• External-facing bots including chat assistants and voice integrations
• Packaging and deploying agent systems for production use

Summary and Next Steps