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Duration 21 hours
Course Outline
Introduction to LLM Agent Systems
- Concepts of LLM agents and multi-agent architecture
- Introduction to the AutoGen framework and its ecosystem
- Agent roles: user proxy, assistant, function caller, and others
Installation and Configuration of AutoGen
- Establishing the Python environment and dependencies
- Fundamentals of AutoGen configuration files
- Integration with LLM providers (OpenAI, Azure, local models)
Agent Design and Role Definition
- Exploring agent types and conversational patterns
- Defining agent objectives, prompts, and directives
- Role-based task distribution and control flow management
Function Calling and Tool Integration
- Registering functions for agent utilization
- Execution of functions autonomously and collaboratively
- Linking external APIs and Python scripts to agents
Conversation Management and Memory
- Session tracking and persistent memory mechanisms
- Inter-agent messaging and token processing
- Management of conversation context and history
End-to-End Agent Workflows
- Developing 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
- Internal automation agents: research, reporting, scripting
- External-facing bots: chat assistants, voice integrations
- Packaging and deploying agent systems for production use
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Knowledge of large language models and prompt engineering
- Practical experience with APIs and automation workflows
Target Audience
- AI engineers
- ML developers
- Automation architects
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.
