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📘 Agentic AI Designing Intelligent Engineering Systems through Principles, Architectures, and Applications A Practical and Academic Guide for Future Engineers Author: Prof. (Dr.) Dalgobind Mahto 🧾 FRONT MATTER Half Title Page Title Page Copyright Page Dedication Foreword Preface Acknowledgements About the Author Abstract / Book Overview List of Figures List of Tables List of Abbreviations Learning Objectives & How to Use This Book 📚 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 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 Cha...

Abstract :  Becoming a data scientist in 2026 requires a multi-disciplinary approach that blends mathematical rigor with AI-assisted development and MLOps. While traditional degrees are still valued, the market increasingly rewards hands-on project experience and the ability to turn raw data into strategic business foresight.   Whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. To move from a data science-adjacent field into data science itself, you’ll need to acquire a specific set of skills, and the most effective way to do this is by enrolling in a better data science course with a structured learning program. So let's dive into the article  How to Become a Data Scientist (2026): Step-by-Step Guide 🎯 STEP 1: Build Strong Foundations (0–2 Months) 🔹 A. Programming Skills Learn Python (primary language) Basics of SQL (very important) 👉 Focus on:...

Publications of Prof.(Dr.) Dalgobind Mahto  International Journal D Mahto & Narinder Singh, “Experimental Study of Process Parameters through Dissimilar Form of Electrodes in EDM Machining”, International Journal of Applied Sciences and Technology, ISSN No2347-6281, Vol.5, Issue No:3, May-June, 2017, pp.24-40  D Mahto & Anjani Kumar, “Qualitative Improvement of Product Quality and Productivity Through Application of Lean Tools: An Observation in Manufacturing Industry”, International Journal of Applied Sciences and Technology, ISSN No2347-6281, Vol.2, Issue No:6, Nov-Dec, 2014, pp.31-60  Sachin Sharma & D Mahto, “Condition Monitoring of Wind Turbines Gear Box”, International Journal of Research Studies in Science, Engineering and Technology ISSN 2349-4751 (Print) & ISSN 2349-476X (Online), Volume 1, Issue 5, Aug 2014, pp. 33-51. Prabhdeep Singh Bajwa & D Mahto, “TRIZ Based Methodical Invention Function Model”,...

Abstract : In 2026, becoming a Machine Learning (ML) Engineer  requires a shift from just "building models" to mastering  end-to-end AI systems , specifically focusing on  Generative AI (GenAI)  and  MLOps . Companies now prioritize "production-ready" engineers who can deploy and maintain models at scale, not just those with theoretical knowledge.   So let's dive into the article  "How to Become a Machine Learning Engineer (2026): Step-by-Step Guide" 🎯 STEP 1: Build Strong Foundations (0–2 Months) 🔹 A. Programming (Must-Have) Learn Python deeply Practice: Loops, functions, OOP File handling, APIs 👉 Libraries: NumPy, Pandas, Matplotlib 🔹 B. Mathematics for ML Focus on: Linear Algebra (vectors, matrices) Probability & Statistics Calculus (basic derivatives) 👉 You don’t need deep theory—focus on intuition + application 🧠 STEP 2: Learn Machine Learning Core (2–4 Months) 🔹 Key Algorithms Linear Regression Logistic Regression Decision ...

Abstract: Preparing for an AI Engineer interview in 2026 requires  balancing core machine learning fundamentals with advanced Generative AI/LLM production skills . Key focus areas include  Python proficiency ,  prompt engineering ,  RAG systems ,  AI agents , and  system design for LLM applications , moving beyond theory to practical, production-ready AI implementation.   Key Focus Areas for 2026 Production AI & LLMs:  Focus on Retrieval Augmented Generation (RAG), Langchain for pipeline construction, vector databases, and agentic workflows (AI agents calling APIs). Core Fundamentals:  Revisit machine learning basics (embeddings, training vs. inference, regularization) and software engineering skills (Git, APIs, JSON). Prompt Engineering & Operations (LLMOps):  Understand advanced prompt techniques (chain-of-thought, few-shot), along with evaluation, cost management, and model monitoring. System Design & Optimizat...

  Abstract : An AI Engineer builds practical applications by integrating artificial intelligence models (like LLMs, OpenAI, Hugging Face) with company data, databases, and existing software, rather than creating models from scratch. They ensure these systems are secure, scalable, and efficient. Key skills include Python, SQL, machine learning fundamentals, and RAG pipelines. How to Become an AI Engineer: Step-by-Step Guide (2026) Artificial Intelligence is one of the most rewarding and future-proof careers today. AI Engineers design systems that can learn, predict, automate, and make intelligent decisions . Companies like Google, Microsoft, Amazon, and OpenAI are constantly hiring skilled AI engineers. This guide provides a clear, practical roadmap to become an AI Engineer from scratch. 🎯 1️⃣ Understand the Role of an AI Engineer An AI Engineer typically: Builds machine learning models Works with large datasets Develops AI-powered applications Deploys models into production Impro...

Chapter 2 Nature and Philosophy of Research 2.1 Introduction Research is not only a process of collecting and analyzing data but also a way of understanding reality through systematic inquiry. Every research study is influenced by certain philosophical assumptions about knowledge, reality, and the methods used to obtain knowledge. These philosophical foundations guide researchers in selecting research designs, methods, and analytical approaches. The philosophy of research refers to the underlying beliefs about how knowledge is created, interpreted, and validated. Understanding research philosophy helps scholars develop clarity about their research approach and choose appropriate methods for conducting their studies. This chapter introduces the nature of research and the key philosophical paradigms that influence research methodologies. 2.2 Nature of Research The nature of research reflects the fundamental characteristics that define research as a scientific acti...

Chapter 3 The Research Process: A Step-by-Step Framework 3.1 Introduction Research is not a random activity; it follows a systematic sequence of steps designed to ensure accuracy, reliability, and meaningful outcomes. This sequence of activities is known as the research process . The research process provides a structured framework that guides researchers from identifying a research problem to presenting research findings. By following a well-defined research process, scholars can conduct studies efficiently, minimize errors, and produce credible results. Understanding the research process is particularly important for PhD scholars and postgraduate students , as it helps them plan and manage their research projects effectively. 3.2 Meaning of the Research Process The research process refers to the systematic series of steps followed by a researcher in order to investigate a problem and generate new knowledge. It involves: Identifying a research problem Reviewing existing l...

Abstract : Theoretical frameworks provide an established, broad, and abstract theory to guide research, while conceptual frameworks offer a specific, often visual, map of variables and their relationships for a particular study Both are essential for structuring research, enhancing rigor, and interpreting findings. So let's dive into the Chapter for more insights... Chapter 9 Conceptual and Theoretical Framework in Research 9.1 Introduction In academic research, a conceptual and theoretical framework provides the foundation for understanding the relationships between variables and guiding the overall research design. These frameworks help researchers organize their ideas, clarify key concepts, and establish the logical structure of a study. For postgraduate and doctoral research, the development of a conceptual or theoretical framework is essential for linking the research problem with existing knowledge and theories. This chapter explains the meaning of conceptual and ...