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 its ecosystem
• Defining agent roles: user proxy, assistant, function caller, and others

Installation and Setup of AutoGen

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

Designing Agents and Assigning Roles

• Exploring agent types and conversation dynamics
• Defining 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 functions autonomously and collaboratively
• Linking external APIs and Python scripts to agents

Managing Conversations and Memory

• Implementing session tracking and persistent memory
• Handling agent-to-agent messaging and token usage
• 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

Application Scenarios and Deployment

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

Recap and Future Directions