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Course Outline

Overview of Artificial Intelligence Agents

  • Definition and scope of AI agents
  • Classifications of agents: Reactive, proactive, and hybrid models
  • Implementation of AI agents in operational environments

Fundamental Design Frameworks

  • Core structural elements of AI agents
  • Mechanisms for agent-environment interaction
  • Introduction to agent-based modeling techniques

Developing Foundational AI Agents

  • Review of available tools and frameworks for agent construction
  • Practical exercise: Developing a foundational chatbot using Rasa
  • Configuration of agent behavioral parameters

Advanced Functional Capabilities

  • Integration of natural language processing capabilities
  • Incorporation of machine learning algorithms
  • Tailoring agent responses for specific user needs

Operational Use Cases

  • Application of AI agents in constituent support services
  • Deployment of virtual assistants and productivity enhancements
  • Utilization of interactive learning modules

Performance Optimization Strategies

  • Improving operational efficiency of AI agents
  • Addressing scalability requirements
  • Evaluating agent effectiveness using Key Performance Indicators (KPIs)

Ethical Considerations and Societal Impact

  • Mitigation of algorithmic biases in AI agents
  • Safeguarding privacy and data integrity
  • Adherence to federal AI regulatory standards

Current Challenges and Future Trajectories

  • Addressing constraints in scalability and performance
  • Ethical frameworks for the deployment of AI agents
  • Emerging developments in AI agent technology

Conclusion and Strategic Outlook

Requirements

  • Foundational knowledge of artificial intelligence principles
  • Proficiency in Python programming

Target Audience

  • Personnel interested in AI technology for government use
  • Information technology specialists
 14 Hours

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