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Duration 14 hours
Course Outline
Revisiting AutoGen Core Concepts
- Defining agents and groups
- Function calling and role chaining
- Identifying limitations of built-in agents and the necessity for customization
Developing Custom Agents with Python
- Defining agent behavior through user_proxy and AssistantAgent subclasses
- Integrating role-specific logic and decision-making processes
- Building reusable agent modules and mixins
Advanced Tool Integration and Routing
- Registering, binding, and invoking tools
- Conditionally directing inputs to designated tools
- Overseeing multi-step toolchains and composite actions
Planning and Context Management
- Designing task decomposers and intermediate planners
- Preserving context across connected agents
- Implementing scoped memory for extended sessions
Error Handling and Recovery Strategies
- Identifying and managing failed or incomplete interactions
- Triggering retries and executing 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
- Comparing role separation and role blending in task allocation
Real-World Deployment Strategies
- Optimizing for performance and cost (token usage, caching)
- Integrating AutoGen workflows into web applications or pipelines
- Addressing security, observability, and user feedback integration
Summary and Future Directions
Requirements
- Strong proficiency in Python programming
- Experience in developing LLM-based applications
- Understanding of function calling and multi-agent system architecture
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.