Obrigado por enviar sua consulta! Um dos membros da nossa equipe entrará em contato com você em breve.
Obrigado por enviar sua reserva! Um dos membros da nossa equipe entrará em contato com você em breve.
Programa do Curso
Review of AutoGen Core Concepts
- Agent and group definitions
- Function calling and role chaining
- Limitations of built-in agents and where customization is needed
Building Custom Agents with Python
- Defining agent behavior using user_proxy and AssistantAgent subclasses
- Injecting role-specific logic and decision-making
- Creating reusable agent modules and mixins
Advanced Tool Integration and Routing
- Tool registration, binding, and invocation
- Conditionally routing inputs to specific tools
- Managing multi-step toolchains and composite actions
Planning and Context Management
- Designing task decomposers and intermediate planners
- Maintaining context across chained agents
- Implementing scoped memory for long-running sessions
Error Handling and Recovery Mechanisms
- Detecting and managing failed or incomplete interactions
- Agent-triggered retries and fallback logic
- Logging, debugging, and response validation
Multi-Agent Collaboration with Custom Roles
- Coordinating specialists within dynamic agent groups
- Orchestrating reasoning loops and cooperative workflows
- Role separation vs. role blending in task assignments
Real-World Deployment Strategies
- Optimizing for performance and cost (token use, caching)
- Embedding AutoGen workflows into web apps or pipelines
- Security, observability, and user feedback integration
Summary and Next Steps
Requisitos
- Proficiency in Python programming
- Experience building with LLM-based applications
- Familiarity with function calling and multi-agent system design
Audience
- Senior developers
- Platform engineers
- AI architects
14 Horas
Declaração de Clientes (1)
Treinador respondendo perguntas na hora.
Adrian
Curso - Agentic AI Unleashed: Crafting LLM Applications with AutoGen
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