Foundations of Agentic AI: Concepts, Prompting & Responsible Design Training Course
Agentic AI refers to systems that act autonomously to achieve goals, integrating reasoning, memory, and tool use. This course provides a structured introduction to agentic AI concepts, focusing on prompt engineering, architectural design patterns, and responsible use practices. Participants will gain foundational knowledge for building, guiding, and deploying agents effectively and safely.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to understand how to design, prompt, and manage responsible agentic systems using practical frameworks and examples.
By the end of this training, participants will be able to:
- Explain the core principles and lifecycle of agentic AI systems.
- Apply prompt engineering techniques for effective task completion.
- Design simple agent workflows using tool access and decision logic.
- Implement safety, governance, and responsible-use considerations in AI agents.
- Build a prototype agent using open frameworks and Python.
Format of the Course
- Interactive lecture and guided demonstration.
- Hands-on exercises and coding practice.
- Collaborative discussions and case-based activities.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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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
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Autonomous Decision-Making with Agentic AI
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