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Kurzusleírás
Introduction to LLM Agent Systems
- LLM agents and multi-agent architecture concepts
- Overview of AutoGen framework and ecosystem
- Agent roles: user proxy, assistant, function caller, and more
Installing and Configuring AutoGen
- Setting up the Python environment and dependencies
- AutoGen configuration file basics
- 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 use
- 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
Követelmények
- An 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 Órák
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Adrian
Kurzus - Agentic AI Unleashed: Crafting LLM Applications with AutoGen
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