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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
- Foundational knowledge of AI or machine learning concepts.
- Familiarity with Python syntax and scripting.
- Experience working with data or API-driven 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)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
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
