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 Duration 14 hours

Course Outline

Refresher on AutoGen Core Concepts

  • Defining agents and groups
  • Function calling and role chaining
  • Identifying limitations of built-in agents and areas requiring customization

Creating Custom Agents in Python

  • Defining agent behavior through user_proxy and AssistantAgent subclasses
  • Incorporating role-specific logic and decision-making capabilities
  • Developing reusable agent modules and mixins

Advanced Tool Integration and Routing

  • Registering, binding, and invoking tools
  • 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 across chained agents
  • Implementing scoped memory for long-running sessions

Error Handling and Recovery Strategies

  • 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
  • Balancing role separation versus role blending in task assignments

Real-World Deployment Strategies

  • Optimizing for performance and cost (including token usage and caching)
  • Integrating AutoGen workflows into web applications or pipelines
  • Ensuring security, observability, and integrating user feedback

Requirements

  • Strong proficiency in Python programming
  • Practical experience developing LLM-based applications
  • Understanding of function calling and multi-agent system architectures

Target Audience

  • Senior developers
  • Platform engineers
  • AI architects

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