Agentic AI: Designing Intelligent Engineering Systems through Principles, Architectures, and Applications; A Practical and Academic Guide for Future Engineers

📘 Agentic AI

Designing Intelligent Engineering Systems through Principles, Architectures, and Applications

A Practical and Academic Guide for Future Engineers

Author: Prof. (Dr.) Dalgobind Mahto

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🧾 FRONT MATTER

1. Half Title Page

2. Title Page

3. Copyright Page

4. Dedication

5. Foreword

6. Preface

7. Acknowledgements

8. About the Author

9. Abstract / Book Overview

10. List of Figures

11. List of Tables

12. List of Abbreviations

13. Learning Objectives & How to Use This Book

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📚 CORE CHAPTERS

🔹 UNIT I: Foundations of Agentic AI

Chapter 1: Introduction to Agentic AI

• Evolution of Artificial Intelligence → From Reactive to Agentic Systems

• Definition and Scope of Agentic AI

• Characteristics of Intelligent Agents

• Agentic AI vs Traditional AI

• Real-world Engineering Use Cases

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Chapter 2: Fundamentals of Intelligent Agents

• Agent Definition and Types (Reactive, Deliberative, Hybrid)

• Rationality and Autonomy

• Environment Types (Deterministic, Stochastic, Dynamic)

• Agent-Environment Interaction Models

• Performance Measures

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Chapter 3: Agent Architectures

• Simple Reflex Agents

• Model-Based Agents

• Goal-Based and Utility-Based Agents

• Hybrid Architectures

• BDI (Belief-Desire-Intention) Architecture

• Comparison of Architectures

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🔹 UNIT II: Design & Development of Agentic Systems

Chapter 4: Agent Design Principles

• Problem Formulation

• State Space Representation

• Decision-Making Strategies

• Planning and Scheduling

• Learning in Agents

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Chapter 5: Multi-Agent Systems (MAS)

• Introduction to MAS

• Agent Communication Protocols

• Cooperation, Coordination, and Negotiation

• Distributed Problem Solving

• Swarm Intelligence Basics

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Chapter 6: Agent Communication & Protocols

• Communication Languages (ACL, KQML)

• Ontologies and Knowledge Sharing

• Interaction Protocols

• Trust and Reputation Systems

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Chapter 7: Tools and Frameworks for Agentic AI

• Overview of Development Platforms

• JADE Framework

• Python-based Agent Frameworks

• Integration with Machine Learning Libraries

• Simulation Environments

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🔹 UNIT III: Learning, Reasoning, and Intelligence

Chapter 8: Machine Learning in Agentic AI

• Supervised, Unsupervised, Reinforcement Learning

• Deep Reinforcement Learning

• Adaptive Agents

• Case Studies

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Chapter 9: Planning and Decision-Making

• Classical Planning Techniques

• Heuristic Search

• Markov Decision Processes (MDPs)

• Game Theory Basics

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Chapter 10: Knowledge Representation & Reasoning

• Logic-Based Representation

• Semantic Networks

• Rule-Based Systems

• Uncertainty Handling

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🔹 UNIT IV: Engineering Applications of Agentic AI

Chapter 11: Agentic AI in Smart Manufacturing

• Industry 4.0 Integration

• Autonomous Production Systems

• Predictive Maintenance

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Chapter 12: Agentic AI in Robotics and Automation

• Autonomous Robots

• Human-Robot Interaction

• Control Systems

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Chapter 13: Agentic AI in Smart Cities and IoT

• Intelligent Traffic Systems

• Smart Energy Management

• Urban Planning

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Chapter 14: Agentic AI in Healthcare Engineering

• Clinical Decision Support Systems

• Personalized Medicine

• Medical Robotics

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🔹 UNIT V: Advanced Topics & Future Directions

Chapter 15: Ethical, Legal, and Social Implications

• AI Ethics and Responsibility

• Bias and Fairness

• Data Privacy and Security

• Regulatory Frameworks

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Chapter 16: Explainable and Trustworthy Agentic AI

• Explainability Techniques

• Transparency in Decision-Making

• Human-in-the-Loop Systems

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Chapter 17: Emerging Trends in Agentic AI

• Generative AI + Agents

• Autonomous Systems Engineering

• Digital Twins

• Edge AI and Real-Time Agents

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Chapter 18: Future of Engineering with Agentic AI

• AI-Driven Engineering Design

• Self-Optimizing Systems

• Research Opportunities

• Career Pathways for Engineers

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🧪 PEDAGOGICAL FEATURES (IN EACH CHAPTER)

• Learning Objectives

• Key Concepts

• Illustrations / Diagrams

• Case Studies

• Worked Examples

• Review Questions (Short + Long)

• Numerical / Analytical Problems

• Mini Projects / Lab Exercises

• Further Reading

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📎 BACK MATTER

1. Appendix A: Mathematical Foundations for Agentic AI

2. Appendix B: Programming Basics (Python for Agents)

3. Appendix C: Case Studies Compilation

4. Appendix D: Tools & Software Installation Guides

5. Glossary of Terms

6. List of Acronyms

7. References (Chapter-wise / Consolidated)

8. Bibliography

9. Index

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🎯 Strength of This Structure

• Fully aligned with B.Tech / M.Tech / AI specialization courses

• Balanced: Theory + Design + Practical + Applications

• Suitable for textbook + reference + competitive exams

• Ready for international publishers (Springer, Elsevier, Wiley)