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Course Outline

Introduction to Agentic AI Systems

  • Defining Agentic AI and Its Capabilities
  • Key Differences Between Rule-Based AI and Autonomous AI
  • Use Cases and Industry Applications for Government

Architecting Agentic AI Systems for Government

  • Frameworks and Tools for Building Autonomous AI
  • Designing AI Agents with Goal-Driven Capabilities
  • Implementing Memory, Context-Awareness, and Adaptability in Government Systems

Developing AI Agents with Python and APIs for Government

  • Building AI Agents Using OpenAI and DeepSeek APIs
  • Integrating AI Models with External Data Sources for Enhanced Decision-Making
  • Handling API Responses and Improving Agent Interactions for Government Operations

Optimizing Multi-Agent Collaboration for Government

  • Designing AI Agents for Cooperative and Competitive Tasks in Public Sector Applications
  • Managing Agent Communication and Task Delegation for Efficient Governance
  • Scaling Multi-Agent Systems for Real-World Government Applications

Enhancing Decision-Making in Agentic AI for Government

  • Reinforcement Learning and Self-Improving AI Agents for Public Sector Operations
  • Planning, Reasoning, and Long-Term Goal Execution in Government Contexts
  • Balancing Automation with Human Oversight in Government Decision-Making

Security, Ethics, and Compliance in Agentic AI for Government

  • Addressing Biases and Ensuring Responsible AI Deployment for Government Services
  • Security Measures for AI-Driven Decision-Making in the Public Sector
  • Regulatory Considerations for Autonomous AI Systems in Government

Future Trends in Agentic AI for Government

  • Advancements in AI Autonomy and Self-Learning Systems for Enhanced Government Services
  • Expanding AI Agent Capabilities with Multimodal Learning for Government Applications
  • Preparing for the Next Generation of Autonomous AI in the Public Sector

Summary and Next Steps for Government

Requirements

  • A foundational understanding of artificial intelligence and machine learning principles
  • Proficiency in Python programming
  • Knowledge of integrating AI models through API-based methods

Audience

  • Artificial intelligence engineers focused on the development of autonomous systems for government and private sector applications
  • Machine learning researchers investigating multi-agent AI frameworks
  • Software developers implementing AI-driven automation solutions
 21 Hours

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