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

Installation and AutoGen Configuration

• Setting up the Python environment and required dependencies.
• Basics of AutoGen configuration files.
• Connecting to LLM providers such as OpenAI, Azure, or local models.

Agent Design and Role Assignment

• Understanding different agent types and conversation patterns.
• Defining agent objectives, prompts, and operational instructions.
• Managing role-based task delegation and control flow.

Function Calling and Tool Integration

• Registering functions for agent utilization.
• Executing functions autonomously and collaboratively.
• Integrating external APIs and Python scripts with agents.

Conversation Management and Memory

• Implementing session tracking and persistent memory.
• Handling agent-to-agent messaging and token management.
• Managing conversation context and historical data.

End-to-End Agent Workflows

• Creating 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 for production environments.

Summary and Next Steps