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Duration 14 hours
Course Outline
Refresher on AutoGen Core Concepts
- Defining agents and groups
- Function calling and role chaining
- Identifying limitations of built-in agents and areas requiring customization
Creating Custom Agents in Python
- Defining agent behavior through user_proxy and AssistantAgent subclasses
- Incorporating role-specific logic and decision-making capabilities
- Developing reusable agent modules and mixins
Advanced Tool Integration and Routing
- Registering, binding, and invoking tools
- Conditionally directing 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 Strategies
- Detecting and managing failed or incomplete interactions
- Implementing agent-triggered retries and fallback logic
- Logging, debugging, and validating responses
Multi-Agent Collaboration with Custom Roles
- Coordinating specialists within dynamic agent groups
- Orchestrating reasoning loops and cooperative workflows
- Balancing role separation versus role blending in task assignments
Real-World Deployment Strategies
- Optimizing for performance and cost (including token usage and caching)
- Integrating AutoGen workflows into web applications or pipelines
- Ensuring security, observability, and integrating user feedback
Requirements
- Strong proficiency in Python programming
- Practical experience developing LLM-based applications
- Understanding of function calling and multi-agent system architectures
Target Audience
- Senior developers
- Platform engineers
- AI architects
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.