Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

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

Installation and Configuration of AutoGen

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

Agent Design and Role Definition

• Examining agent types and interaction patterns
• Establishing agent objectives, prompts, and directives
• Implementing role-based task delegation and control flow

Function Calling and Tool Integration

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

Conversation Management and Memory

• Tracking sessions and maintaining persistent memory
• Handling agent-to-agent communication and token management
• Optimizing 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, scripting
• External-facing bots: chat assistants, voice integrations
• Packaging and deploying agent systems for production environments

Summary and Next Steps