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
• Exploring agent roles: user proxy, assistant, function caller, and others

AutoGen Setup and Configuration

• Establishing the Python environment and required dependencies
• Fundamentals of AutoGen configuration files
• Integrating with LLM providers (OpenAI, Azure, and local models)

Agent Design and Role Definition

• Analyzing agent types and interaction patterns
• Crafting agent objectives, prompts, and instructions
• Implementing role-based task delegation and control flow

Function Calling and Tool Connectivity

• Registering functions for agent utilization
• Executing functions autonomously or collaboratively
• Linking external APIs and Python scripts to agents

Conversation Control and Memory Management

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

Comprehensive Agent Workflows

• Creating multi-step collaborative tasks (such as document analysis or 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 including chat assistants and voice integrations
• Packaging and deploying agent systems for production use

Recap and Future Steps