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Course Outline
Review of AutoGen Core Concepts
- Definitions of agents and groups.
- Function calling and role chaining.
- Limitations of built-in agents and scenarios requiring customization.
Building Custom Agents with Python
- Defining agent behavior using user_proxy and AssistantAgent subclasses.
- Injecting role-specific logic and decision-making processes.
- 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 versus role blending in task assignments.
Real-World Deployment Strategies
- Optimizing for performance and cost (token usage, caching).
- Embedding AutoGen workflows into web applications or pipelines.
- Security, observability, and user feedback integration.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Experience in developing LLM-based applications.
- Familiarity with function calling and multi-agent system design.
Audience
- Senior developers.
- Platform engineers.
- AI architects.
14 Hours
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.
