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Schița de curs
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
Cerințe
- 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 ore
Mărturii (1)
Formatorul răspunde la întrebări în timp real.
Adrian
Curs - Agentic AI Unleashed: Crafting LLM Applications with AutoGen
Tradus de catre o masina
