Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

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

Installation and Configuration of AutoGen

• Setting up the Python environment and required dependencies
• Understanding AutoGen configuration files
• Integrating with LLM providers such as OpenAI, Azure, and local models

Agent Design and Role Definition

• Exploring agent types and conversation dynamics
• Crafting agent goals, prompts, and instructions
• Implementing role-based task delegation and control flow

Function Calling and Tool Integration

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

Conversation Management and Memory Handling

• Tracking sessions and implementing persistent memory
• Handling inter-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-making chains
• Debugging and optimizing agent performance

Use Cases and Deployment Strategies

• Developing internal automation agents for research, reporting, and scripting
• Creating external-facing bots, including chat assistants and voice integrations
• Packaging and deploying agent systems for production environments

Summary and Next Steps