Gracias por enviar su consulta! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Gracias por enviar su reserva! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Programa del Curso
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
Requerimientos
- 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 Horas
Testimonios (1)
Entrenador respondiendo preguntas al vuelo.
Adrian
Curso - Agentic AI Unleashed: Crafting LLM Applications with AutoGen
Traducción Automática
