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Course Outline
Introduction to Agentic AI for Government
- Defining agentic AI and its relationship to traditional AI systems used in public sector operations
- Overview of reasoning, memory, and goal-driven architectures relevant to government applications
- Key use cases and industry applications tailored for government workflows
Core Concepts and Design Patterns for Government
- The agent loop: perception, reasoning, and action within the context of public sector operations
- Single-agent vs. multi-agent systems in government environments
- Environment interaction and tool invocation for government-specific tasks
Prompt Engineering Fundamentals for Government
- Designing effective prompts for reasoning and task decomposition to enhance public sector workflows
- Using examples, constraints, and roles for better control in government applications
- Debugging and iterating prompts systematically to ensure reliability in government systems
Building Simple Agentic Workflows for Government
- Implementing an agent loop in Python for government use cases
- Integrating with APIs and simple tools to support public sector operations
- Managing agent state and memory to ensure data integrity in government systems
Responsible Design and Safety Practices for Government
- Ethical considerations and responsible use of agents in the public sector
- Bias, transparency, and accountability in AI systems used by government agencies
- Access control, data protection, and content safety to ensure compliance with government regulations
Hands-on Project: Designing a Responsible Agent for Government
- Defining the problem scope and objectives within the context of public sector needs
- Developing the prompt and control logic to align with government workflows
- Testing, refining, and evaluating agent behavior to meet government standards
Summary and Next Steps for Government
Requirements
- A foundational understanding of artificial intelligence or machine learning concepts
- Proficiency in Python syntax and scripting
- Practical experience working with data or API-based applications
Audience for Government
- Data scientists new to the development of agentic AI systems
- Junior machine learning engineers interested in applied agent architectures
- Technology managers aiming to gain insight into agent design and safety principles
14 Hours
Testimonials (3)
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Autonomous Decision-Making with Agentic AI
practical exercises
