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Course Outline
Review of AutoGen Core Concepts
- Understanding agent and group definitions.
- Function calling and role chaining mechanisms.
- Recognizing the limitations of built-in agents and identifying where customization is required.
Building Custom Agents with Python
- Defining agent behavior via user_proxy and AssistantAgent subclasses.
- Injecting role-specific logic and decision-making capabilities.
- Developing reusable agent modules and mixins.
Advanced Tool Integration and Routing
- Managing tool registration, binding, and invocation.
- Conditionally routing inputs to appropriate tools.
- Orchestrating 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 long-running sessions.
Error Handling and Recovery Mechanisms
- 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.
- Navigating role separation versus role blending in task assignments.
Real-World Deployment Strategies
- Optimizing for performance and cost efficiency (token usage, caching).
- Embedding AutoGen workflows into web applications or data pipelines.
- Integrating security measures, observability tools, and user feedback loops.
Summary and Next Steps
Requirements
- Strong proficiency in Python programming.
- Hands-on experience developing applications based on large language models.
- Familiarity with function calling and the design of multi-agent systems.
Target 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.
