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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 and private sector operations
Architecting Agentic AI Systems
- Frameworks and tools for building autonomous AI for government and other sectors
- Designing AI agents with goal-driven capabilities to enhance operational efficiency
- Implementing memory, context-awareness, and adaptability in AI systems for improved performance
Developing AI Agents with Python and APIs
- Building AI agents using OpenAI and DeepSeek APIs to support diverse applications
- Integrating AI models with external data sources to enhance decision-making for government and industry
- Handling API responses and improving agent interactions to ensure reliable performance
Optimizing Multi-Agent Collaboration
- Designing AI agents for cooperative and competitive tasks in various environments
- Managing agent communication and task delegation to optimize resource utilization
- Scaling multi-agent systems for real-world applications, including those for government agencies
Enhancing Decision-Making in Agentic AI
- Reinforcement learning and self-improving AI agents to enhance decision-making processes
- Planning, reasoning, and long-term goal execution for more effective outcomes
- Balancing automation with human oversight to ensure responsible use of AI technologies
Security, Ethics, and Compliance in Agentic AI
- Addressing biases and ensuring responsible AI deployment for government and private sector applications
- Security measures to protect AI-driven decision-making processes
- Regulatory considerations for the deployment of autonomous AI systems in various sectors
Future Trends in Agentic AI
- Advancements in AI autonomy and self-learning systems to support evolving needs
- Expanding AI agent capabilities with multimodal learning for enhanced functionality
- Preparing for the next generation of autonomous AI to meet future challenges and opportunities
Summary and Next Steps
Requirements
- Fundamental knowledge of artificial intelligence and machine learning principles
- Proficiency in Python programming
- Experience with integrating AI models through APIs
Audience for Government
- AI engineers working on autonomous systems development
- Machine learning researchers investigating multi-agent AI frameworks
- Developers implementing AI-driven automation solutions
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
