Hands-On Learning

Develop practical skills through realistic professional scenarios, agent behaviour investigations and decision-making exercises. Evaluate autonomy, identify risks and create a responsible Agentic AI Adoption Rationale.

Expert Guidance

Build a structured understanding of agentic AI, from agent architectures and cognitive delegation to human oversight, accountability and responsible adoption. Apply clear analytical frameworks to professional challenges.

Flexible Schedule

Learn at your own pace through accessible online lessons, practical activities and worked examples. Develop your understanding of agentic AI alongside your existing professional and personal commitments.

About the Course

Agentic AI represents an important development in artificial intelligence, enabling systems to pursue goals, plan activities, use tools and adapt their actions with varying degrees of autonomy. Foundations of Agentic AI: Autonomy, Agency and Human–AI Collaboration provides a comprehensive introduction to these capabilities and their implications for professional practice. You will explore how agentic systems operate, how they differ from conventional generative AI and how humans can collaborate with increasingly autonomous technologies while retaining meaningful judgement and control. Through realistic scenarios, structured investigations and practical exercises, you will learn to evaluate agent capabilities, recognise limitations, establish appropriate autonomy boundaries and assess opportunities for responsible adoption. The course culminates in an Agentic AI Adoption Rationale, allowing you to bring together your learning into a practical, evidence-informed professional recommendation. Designed for educators, researchers, professional services staff, managers and anyone interested in the future of human–AI collaboration, the course requires no programming experience or specialist technical background.

Meet Your Instructor

Jonathan Wong is the founder of CloudPedagogy and a digital education specialist with over 20 years of experience across higher education, research and digital innovation. His work focuses on turning emerging technologies into practical approaches to learning, analysis and knowledge creation. Through CloudPedagogy, he develops courses, open-source tools, interactive learning systems and applied projects exploring areas including generative and agentic AI, educational technology and digital learning design. His approach emphasises practical experimentation, transparency, critical judgement and responsible use of technology. CloudPedagogy is built around learning by understanding, building and applying: making complex ideas accessible while creating practical tools and workflows that demonstrate how they can be used in real-world contexts.

Curriculum

9 Chapters · 49 Lessons

    1. Welcome to the Course Free preview
    2. (Included in full purchase)

      Understanding the Emergence of Agentic AI

    3. Legal Disclaimer & Terms of Use Free preview

    1. 1.1 From Generative AI to Agentic Systems Free preview
    2. 1.2 Agents, Goals and Autonomy Free preview
    3. 1.3 Reasoning, Planning and Execution Free preview
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      1.4 Tools, Environments and Actions

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      1.5 Learning Activities

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      1.6 Agentic or Not?

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      1.7 Autonomy Boundary Investigation

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      1.8 Agent Execution Reconstruction

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      2.1 The Components of an Agentic System

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      2.2 Memory, Context and State

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      2.3 Single-Agent and Multi-Agent Systems

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      2.4 Understanding Agent Execution

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      2.5 Learning Activities

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      2.6 Architecture Mapping Challenge

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      2.7 Single Agent or Multiple Agents?

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      2.8 Investigating an Agent Trace

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      3.1 Human–Agent Collaboration

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      3.2 Cognitive Delegation and Intellectual Responsibility

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      3.3 Degrees of Human Control

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      3.4 Intellectual Independence in Agentic Environments

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      3.5 Learning Activities

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      3.6 Cognitive Delegation Map

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      3.7 Human Control Design Challenge

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      3.8 Preserving Intellectual Independence

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      4.1 Identifying Suitable Tasks for Agentic AI

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      4.2 Understanding Agent Capabilities and Limitations

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      4.3 Recognising Failure and Uncertainty

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      4.4 Comparing Human and Agent Approaches

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      4.5 Learning Activities

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      4.6 Agentic Opportunity Assessment

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      4.7 Failure Investigation

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      4.8 Human–AI–Agent Comparison

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      5.1 Trust, Verification and Evidence

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      5.2 Responsibility and Accountability

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      5.3 The Future of Agentic AI and Human Capability

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      5.4 Developing a Responsible Agentic AI Adoption Rationale

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      5.5 Learning Activities

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      5.6 Evidence and Trust Assessment

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      5.7 Accountability Scenario

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      5.8 Future Scenarios Challenge

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      6.1 Introduction to the Final Integrative Activity

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      6.2 Agentic AI Adoption Rationale

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      6.3 Reviewing and Refining Your Adoption Rationale

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      6.4 Course Reflection and Next Steps

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      Course Complete: Share Your Feedback

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      End of Course Survey

Unlock Your Genai Potential

Join our course today and unlock the full potential of Generative AI in your research projects. Empower yourself with the knowledge and skills to drive innovation and make a significant impact in your field.

£49.00

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