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Course Outline

Introduction to Agentic AI

  • Defining agentic AI and its distinctions from conventional artificial intelligence systems
  • Overview of reasoning, memory, and goal-driven architectures for government applications
  • Key use cases and industry applications relevant to public sector operations

Core Concepts and Design Patterns

  • The agent loop: perception, reasoning, and action
  • Single-agent vs. multi-agent systems
  • Environment interaction and tool invocation

Prompt Engineering Fundamentals

  • Designing effective prompts for reasoning and task decomposition
  • Using examples, constraints, and roles for better control and governance
  • Debugging and iterating prompts systematically to ensure reliability for government use

Building Simple Agentic Workflows

  • Implementing an agent loop in Python
  • Integrating with APIs and simple tools
  • Managing agent state and memory

Responsible Design and Safety Practices

  • Ethical considerations and responsible use of agents in federal contexts
  • Bias, transparency, and accountability in AI systems
  • Access control, data protection, and content safety measures for government initiatives

Hands-on Project: Designing a Responsible Agent

  • Defining the problem scope and objectives
  • Developing the prompt and control logic for secure implementation
  • Testing, refining, and evaluating agent behavior to ensure compliance and effectiveness

Summary and Next Steps

Requirements

  • Foundational knowledge of artificial intelligence and machine learning principles
  • Proficiency in Python programming language syntax and scripting capabilities
  • Practical experience utilizing data management or API-integrated applications

Audience

  • Data scientists initiating careers in agentic AI development for government
  • Entry-level machine learning engineers investigating applied agent architectural frameworks
  • Technology leadership personnel requiring comprehension of agent design and safety compliance protocols
 14 Hours

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