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Course Outline
Introduction to Agentic AI
- Defining agentic AI and its relationship to traditional AI systems
- Overview of reasoning, memory, and goal-driven architectures
- Key use cases and industry applications
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
- Debugging and iterating prompts systematically
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
- Bias, transparency, and accountability in AI systems
- Access control, data protection, and content safety
Hands-on Project: Designing a Responsible Agent
- Defining the problem scope and objectives
- Developing the prompt and control logic
- Testing, refining, and evaluating agent behavior
Summary and Next Steps
Requirements
- Basic understanding of AI or machine learning concepts
- Familiarity with Python syntax and scripting
- Experience working with data or API-based applications
Audience
- Data scientists new to agentic AI development
- Junior ML engineers exploring applied agent architectures
- Technology managers seeking to understand 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
