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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

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