Get in Touch

Course Outline

Overview of Large Language Model Agent Systems

  • Concepts underlying LLM agents and multi-agent architectures
  • Introduction to the AutoGen framework and its associated ecosystem
  • Agent roles, including user proxy, assistant, function caller, and others

Installation and Configuration of AutoGen

  • Establishing the Python environment and required dependencies
  • Fundamentals of AutoGen configuration files
  • Integration with LLM providers (OpenAI, Azure, and local models)

Agent Design and Role Assignment

  • Understanding agent types and interaction patterns
  • Defining agent objectives, prompts, and operational instructions
  • Task delegation and control flow based on agent roles

Function Calling and Tool Integration

  • Registering functions for use by agents
  • Supporting autonomous and collaborative function execution
  • Linking external APIs and Python scripts to agent capabilities

Conversation Management and Memory

  • Session tracking and persistent memory storage
  • Agent-to-agent messaging and token management
  • Maintenance of conversation context and history

End-to-End Agent Workflows

  • Development of multi-step collaborative tasks (e.g., document analysis, code review)
  • Simulation of user-agent dialogues and decision processes
  • Debugging and optimization of agent performance

Use Cases and Deployment for government

  • Internal automation agents: research, reporting, scripting
  • External-facing applications: chat assistants, voice integrations
  • Packaging and deploying agent systems in production environments

Summary and Next Steps

Requirements

  • Proficiency in Python development
  • Knowledge of large language models and prompt design techniques
  • Practical application of APIs and automated processes

Target Participants

  • AI engineers
  • Machine learning developers
  • Automation architects
 21 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories