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Duration 14 hours
Course Outline
Revisiting AutoGen Core Concepts
- Definitions of agents and groups
- Mechanisms of function calling and role chaining
- Identifying limitations of built-in agents and determining where customization is required
Developing Custom Agents with Python
- Defining agent behavior through user_proxy and AssistantAgent subclasses
- Incorporating role-specific logic and decision-making processes
- Creating reusable agent modules and mixins
Advanced Tool Integration and Routing
- Tool registration, binding, and invocation strategies
- 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 continuity across chained agents
- Implementing scoped memory for extended sessions
Error Handling and Recovery Protocols
- Identifying 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 (token usage, caching)
- Integrating AutoGen workflows into web apps or data pipelines
- Ensuring security, observability, and integrating user feedback
Summary and Future Directions
Requirements
- Strong proficiency in Python programming
- Experience in developing LLM-based applications
- Familiarity with function calling and multi-agent system design
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.