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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
Testimonials (1)
Trainer responding to questions on the fly.