Course Code: advautogen
Duration: 14 hours
Prerequisites:

  • Proficiency in Python programming
  • Experience building with LLM-based applications
  • Familiarity with function calling and multi-agent system design

Audience

  • Senior developers
  • Platform engineers
  • AI architects

Overview:

AutoGen is an open-source framework from Microsoft for building multi-agent applications that use LLMs, tools, memory, and user interaction.

This instructor-led, live training (online or onsite) is aimed at advanced-level developers and architects who wish to design and deploy deeply customized agents using AutoGen’s Python-based APIs, function-calling capabilities, and modular toolchains.

By the end of this training, participants will be able to:

  • Develop custom agents with role-specific logic and tool routing.
  • Build dynamic workflows using advanced function calling and context switching.
  • Implement memory modules and planning frameworks within agent teams.
  • Handle multi-agent error states and adaptive retry mechanisms.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline:

Review of AutoGen Core Concepts

  • Agent and group definitions
  • Function calling and role chaining
  • Limitations of built-in agents and where customization is needed

Building Custom Agents with Python

  • Defining agent behavior using user_proxy and AssistantAgent subclasses
  • Injecting role-specific logic and decision-making
  • 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 vs. role blending in task assignments

Real-World Deployment Strategies

  • Optimizing for performance and cost (token use, caching)
  • Embedding AutoGen workflows into web apps or pipelines
  • Security, observability, and user feedback integration

Summary and Next Steps