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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
Testimonials (2)
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