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Course Outline
Introduction to LLM Agent Systems
- Concepts of LLM agents and multi-agent architecture.
- Overview of the AutoGen framework and its ecosystem.
- Agent roles: user proxy, assistant, function caller, and more.
Installing and Configuring AutoGen
- Setting up the Python environment and dependencies.
- Fundamentals of AutoGen configuration files.
- Connecting to LLM providers (OpenAI, Azure, local models).
Agent Design and Role Assignment
- Understanding agent types and conversation patterns.
- Defining agent goals, prompts, and instructions.
- Role-based task delegation and control flow.
Function Calling and Tool Integration
- Registering functions for agent utilization.
- Autonomous and collaborative function execution.
- Connecting external APIs and Python scripts to agents.
Conversation Management and Memory
- Session tracking and persistent memory.
- Agent-to-agent messaging and token handling.
- Managing conversation context and history.
End-to-End Agent Workflows
- Building multi-step collaborative tasks (e.g., document analysis, code review).
- Simulating user-agent dialogues and decision chains.
- Debugging and refining agent performance.
Use Cases and Deployment
- Internal automation agents: research, reporting, scripting.
- External-facing bots: chat assistants, voice integrations.
- Packaging and deploying agent systems in production.
Summary and Next Steps
Requirements
- Understanding of Python programming.
- Familiarity with large language models and prompt engineering.
- Experience with APIs and automation workflows.
Audience
- AI engineers.
- ML developers.
- Automation architects.
21 Hours
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.
