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Description
As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.
The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
By the final chapter, readers will not only understand agentic systems they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents.
What You Will Learn
- A practical introduction to Machine Learning and Generative AI, tailored for engineers
- Conceptualize, design, and build autonomous AI agents from scratch even with a minimal AI background.
- The core principles of Agentic AI, including goals, environments, actions, and feedback loops
- Understand different Agentic AI frameworks and their applications.
- Integrate agentic systems into real-world applications using hands-on coding examples
- Review strategies for ensu
Part I Core Concepts of Modern AI Systems.- Chapter 1:Introduction: Agentic AI.- Chapter 2:Automation to Autonomy: A New Mindshift.- Chapter 3:Transformer models & LLM architecture.- Chapter 4: The Agentic AI Fundamentals: Goals, Environments, Actions. -Part II Building Blocks of Agentic Systems.- Chapter 5: Architectural Patterns for Agentic Systems.- Chapter 6: Prompting.- Chapter 7: Tools & Frameworks for building Agents.- Chapter 8: Safety, Alignment, and Robustness in Agents.- Part III Applications & Engineering Practice.- Chapter 9: Real-world Domain-Specific Use Cases of Agentic AI.- Chapter 10: Build Your First AI Agent - Hands-on Coding.- Chapter 11: Engineering Agent Feedback Loops.- Chapter 12: Collaborative Agents (Multi-Agent Systems, Human-AI Teaming).- Chapter 13: Testing, Debugging, and Deployment Considerations.- Chapter 14: The Road Ahead: Open Challenges and Responsible.
Dhivya Nagasubramanian is an AI/ML practitioner with extensive experience leading digital transformation and automation initiatives in the financial services sector. In her current role, she focuses on delivering practical, business-aligned AI solutions at scale.
Her background spans a range of roles across information technology, data science, enterprise architecture, and engineering. Over the years, she has worked on building and deploying AI systems that solve complex, real-world problems particularly in environments where reliability, compliance, and scale matter.
Dhivya holds a postgraduate degree in Business Analytics and has completed executive coursework in business communication from Harvard. She actively participates and volunteers at Women in AI community and Executive council for leading change where she contributes to ongoing discussions around AI strategy, education, and innovation. This book brings together her practical perspective on Agentic AI and aims to make the topic approachable for engineers looking to get started in the field.
