Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

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

Installation and AutoGen Configuration

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

Agent Design and Role Assignment

• Understanding agent types and effective conversation patterns.
• Setting agent goals, prompts, and operational instructions.
• Implementing role-based task delegation and control flows.

Function Calling and Tool Integration

• Registering functions for agent access.
• Managing autonomous and collaborative function execution.
• Integrating external APIs and Python scripts into agent workflows.

Conversation Management and Memory

• Session tracking and persistent memory implementation.
• Agent-to-agent messaging and token management.
• Controlling 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 for production environments.

Summary and Next Steps