Course Outline

Introduction to LLM Agent Systems for Government

  • Concepts of LLM agents and multi-agent architecture
  • Overview of the AutoGen framework and its ecosystem for government applications
  • Agent roles including user proxy, assistant, function caller, and more

Installing and Configuring AutoGen for Government Use

  • Setting up the Python environment and required dependencies
  • Basics of the AutoGen configuration file for government systems
  • Connecting to LLM providers such as OpenAI, Azure, and local models for government operations

Agent Design and Role Assignment for Government Applications

  • Understanding different agent types and conversation patterns for public sector use
  • Defining agent goals, prompts, and instructions tailored to government tasks
  • Role-based task delegation and control flow in government workflows

Function Calling and Tool Integration for Government Operations

  • Registering functions for agent use in government systems
  • Autonomous and collaborative function execution for public sector tasks
  • Connecting external APIs and Python scripts to agents for government applications

Conversation Management and Memory for Government Agents

  • Session tracking and persistent memory management for government use cases
  • Agent-to-agent messaging and token handling in government systems
  • Managing conversation context and history for public sector operations

End-to-End Agent Workflows for Government Tasks

  • Building multi-step collaborative tasks such as document analysis and code review for government
  • Simulating user-agent dialogues and decision chains in public sector scenarios
  • Debugging and refining agent performance for government applications

Use Cases and Deployment of Government Agents

  • Internal automation agents for research, reporting, and scripting in government agencies
  • External-facing bots for chat assistants and voice integrations in public services
  • Packaging and deploying agent systems in production environments for government use

Summary and Next Steps for Government Implementation

Requirements

  • An understanding of Python programming for government applications
  • Familiarity with large language models and prompt engineering in a public sector context
  • Experience with APIs and automation workflows tailored for government use

Audience

  • AI engineers working in the public sector
  • ML developers supporting government initiatives
  • Automation architects focused on government projects
 21 Hours

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