Building LLM Agent Systems with AutoGen Training Course

Foundations of LLM Agent Systems

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

Setup and Configuration of AutoGen

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

Agent Architecture and Role Definition

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

Function Invocation and Tool Integration

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

Conversation Control and Memory Management

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

Comprehensive Agent Workflows

• Creating multi-step collaborative tasks (e.g., document analysis, code review)
• Simulating user-agent interactions and decision sequences
• Debugging and optimizing agent performance

Applications and Deployment

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

Recap and Future Pathways