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 Duration 14 hours

Course Outline

Revisiting AutoGen Core Concepts

  • Definitions and structures for agents and groups
  • Mechanisms for function calling and role chaining
  • Identifying limitations of built-in agents and the necessity for customization

Creating Custom Agents with Python

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

Advanced Tool Integration and Routing Strategies

  • Processes for tool registration, binding, and invocation
  • Conditionally directing inputs to designated tools
  • Overseeing multi-step toolchains and composite actions

Planning and Context Management

  • Architecting task decomposers and intermediate planners
  • Persisting context 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 specialized agents within dynamic 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 efficiency (including token usage and caching)
  • Integrating AutoGen workflows into web applications or data pipelines
  • Ensuring security, observability, and user feedback integration

Requirements

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

Target Audience

  • Senior software developers
  • Platform engineers
  • AI architects

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