Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

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

Setting Up AutoGen

• Configuring the Python environment and necessary dependencies
• Basics of AutoGen configuration files
• Integrating with LLM providers such as OpenAI, Azure, and local models

Agent Design and Role Definition

• Exploring agent types and conversation patterns
• Establishing agent goals, prompts, and directives
• 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

Conversation Management and Memory

• Tracking sessions and maintaining persistent memory
• Handling agent-to-agent messaging and tokens
• Managing 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 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 in production environments

Summary and Next Steps