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Building Agentic AI Systems

You're reading from   Building Agentic AI Systems Create intelligent, autonomous AI agents that can reason, plan, and adapt

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Product type Paperback
Published in Apr 2025
Publisher Packt
ISBN-13 9781803238753
Length 288 pages
Edition 1st Edition
Concepts
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Authors (2):
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Wrick Talukdar Wrick Talukdar
Author Profile Icon Wrick Talukdar
Wrick Talukdar
Anjanava Biswas Anjanava Biswas
Author Profile Icon Anjanava Biswas
Anjanava Biswas
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Foundations of Generative AI and Agentic Systems
2. Chapter 1: Fundamentals of Generative AI FREE CHAPTER 3. Chapter 2: Principles of Agentic Systems 4. Chapter 3: Essential Components of Intelligent Agents 5. Part 2: Designing and Implementing Generative AI-Based Agents
6. Chapter 4: Reflection and Introspection in Agents 7. Chapter 5: Enabling Tool Use and Planning in Agents 8. Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach 9. Chapter 7: Effective Agentic System Design Techniques 10. Part 3: Trust, Safety, Ethics, and Applications
11. Chapter 8: Building Trust in Generative AI Systems 12. Chapter 9: Managing Safety and Ethical Considerations 13. Chapter 10: Common Use Cases and Applications 14. Chapter 11: Conclusion and Future Outlook 15. Index 16. Other Books You May Enjoy

Answers

  1. Trust is essential for the widespread and responsible adoption of generative AI. If users lack confidence in the system’s decision-making process, they will be reluctant to rely on its outputs. Trust influences how users interact with AI, whether they share feedback, provide data, or even adopt the technology in the first place. A lack of trust can lead to skepticism, resistance, and even misuse of AI systems.
  2. Transparency and explainability help users understand how an AI system arrives at its decisions, making it more trustworthy. Transparency operates at two levels:
    • Algorithmic transparency – Openness about the model’s architecture, training data, and biases ensures that AI systems can be assessed for reliability and fairness.
    • Presentation transparency (explainability) – AI should clearly communicate its reasoning so users can interpret and trust the output. Techniques like attention visualization, saliency maps, and natural language explanations...
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