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Agentic AI Explained

From Neural Networks to Autonomous Agents: How It Works, How to Build It, and What It Means

Agentic AI Explained
This book is 100% completeLast updated on 2026-09-20
Steve Publications

Agentic AI is everywhere, but what does it actually mean? This book cuts through the hype and explains how AI agents work, how they are built and where they can go wrong. Starting from the basics, it gives you a clear, practical understanding of the technology and what it takes to trust an agent in the real world.

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About the Book

You have heard the term "agentic AI" everywhere. News headlines promise robots that work for you. Product managers ask their teams to build "an agent" for everything. Vendors sell you autonomy, and sometimes you are not sure whether to believe them. This book explains what agentic AI actually is, how it works under the hood, what it can and cannot do reliably, how real systems are designed and built, and what risks and trade-offs you should understand before trusting one with anything important. It assumes you know nothing about artificial intelligence and walks you from the ground up, with patience, concrete examples, and no hype. By the time you finish, you will be able to cut through marketing language, think clearly about where agents can help, and understand what people mean when they say something is or is not ready for production.

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Agentic EngineeringArtificial IntelligenceContext EngineeringLarge language models

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About the Author

Steve Publications

Steve is a technology professional with more than 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. Throughout his career, he has designed, secured, analyzed and tested complex software and infrastructure, with a particular focus on understanding how systems fail and how they can be made more secure.

Outside of work, Steve enjoys sharing knowledge with the technology community. He collaborates with researchers, industry experts and technology professionals to write practical books covering software development, cybersecurity, cloud computing, networking, DevOps, artificial intelligence and enterprise technologies. His books focus on practical learning through clear explanations, real-world examples and hands-on exercises. With more than two decades of industry experience, his goal is to help IT professionals, students and technology enthusiasts build useful skills and stay current in a rapidly changing industry.

We believe readers deserve to know how our books are created. Most of our authors are not native English speakers, so we use AI to help translate, proofread manuscripts, fix grammar, improve sentence structure and make technical explanations easier to read. AI is used as an editing tool only. It does not replace the research, technical knowledge or hands-on experience behind our books. Some of our authors also prefer to remain anonymous for privacy or professional reasons. In those cases, we publish their work under a different name. The author's name may be different, but the quality of the content and our review process remain the same.

Every book is written, reviewed and maintained by experienced technology professionals, with contributions from our private technical community of more than 420 engineers and researchers. We spend far more time validating technical accuracy and keeping our content up to date than generating text. We are always interested in working with experienced professionals who have deep expertise in a particular technology or domain. If you would like to publish a book with us or help review an existing manuscript, we'd love to hear from you. Send us a message describing your area of expertise. We are especially interested in niche technologies, specialized skills and emerging topics that are underrepresented in existing technical literature.

If you look through the contents of our books, you'll see practical examples, detailed explanations and material that is regularly updated. Our goal is to publish books that professionals can actually rely on, not low-effort AI-generated content. If you ever feel that one of our books does not meet that standard, Leanpub offers a 60-day money-back guarantee. Feel free to request a refund if you are not satisfied with your purchase.

Contents

Table of Contents

From Neural Networks to Autonomous Agents: How It Works, How to Build It, and What It Means

Introduction

  1. What this book covers
  2. How to read this book

Chapter 1: What Is Intelligence, Anyway?

  1. An Agent at Work
  2. Defining Intelligence
  3. The Long History of AI
  4. Narrow AI, General AI, and the Hype Gap
  5. Why This Book Exists

Chapter 2: How Machines Learn

  1. Patterns Everywhere
  2. Training, Testing, and the Illusion of Understanding
  3. The Magic of Gradient Descent
  4. When Learning Fails
  5. From Rules to Learning

Chapter 3: The Brain Inspired the Network

  1. Biological Neurons vs. Artificial Neurons
  2. Layers of Abstraction
  3. Deep Learning and the Scaling Phenomenon
  4. Specialized Architectures
  5. What Networks Actually Represent: Embeddings and the Geometry of Meaning

Chapter 4: The Transformer Revolution

  1. Why Previous Models Hit a Wall
  2. Attention Explained
  3. Self-Attention and Context
  4. Encoder and Decoder
  5. The Big Paper

Chapter 5: What Is a Large Language Model?

  1. Language Modeling as a Prediction Game
  2. The Data Diet
  3. Size, Scale, and Emergent Abilities
  4. Foundation Models and the Shift in AI
  5. What LLMs Are Not

Chapter 6: Inside the LLM Brain

  1. Tokenization
  2. Context Windows and Working Memory
  3. The Inference Dance
  4. Temperature, Sampling, and Why LLMs Are Probabilistic
  5. The Hidden Space

Chapter 7: Prompting and the Art of Talking to LLMs

  1. The Prompt as Program
  2. Zero-Shot, Few-Shot, and Example-Based Prompting
  3. Chain of Thought and Structured Reasoning
  4. System Prompts and Role-Playing
  5. When Prompting Fails

Chapter 8: Reasoning, Hallucination, and the Illusion of Understanding

  1. Statistical Reasoning vs. Human Reasoning
  2. Chain of Thought as a Tool
  3. The Hallucination Problem
  4. Confidently Wrong
  5. Tools for Checking and Grounding

Chapter 9: From Chatbots to Agents

  1. The Chatbot Paradigm
  2. The Agent Paradigm
  3. What Makes an Agent “Agentic”
  4. A Simple Example: Email Assistant vs. Email Agent
  5. The Spectrum of Autonomy

Chapter 10: The Anatomy of an AI Agent

  1. The Core Loop: Perceive, Think, Act
  2. The Brain: The LLM as the Central Reasoning Component
  3. The Toolbox: Tools, APIs, and How Agents Call External Functions
  4. The Memory System: Short-Term Context, Long-Term Storage, and Knowledge Retrieval
  5. The Orchestrator: What Coordinates the Agent’s Behavior and Enforces Constraints

Chapter 11: How Agents Use Tools

  1. Function Calling Explained
  2. Web Search and Browsing
  3. Code as a Tool
  4. Integrating with Business Systems
  5. Tool Selection and Chaining

Chapter 12: Memory, Knowledge, and Retrieval

  1. The Memory Problem
  2. Short-Term Memory: Conversational Context and Working Memory
  3. Long-Term Memory: Vector Databases, Knowledge Bases, and Persistent Storage
  4. Retrieval-Augmented Generation
  5. Embeddings and Similarity Search

Chapter 13: Planning and Decision Making

  1. Goal Decomposition
  2. Planning Algorithms
  3. Reflection and Self-Correction
  4. Uncertainty and Adaptation
  5. The Limits of AI Planning

Chapter 14: Multi-Agent Systems

  1. Why Multiple Agents?
  2. Communication Protocols
  3. Roles and Responsibilities
  4. Multi-Agent Frameworks
  5. When More Agents Help and When They Hurt

Chapter 15: Building an Agentic System, Step by Step

  1. Problem Definition
  2. Model Selection
  3. Designing the Agent Architecture
  4. Prompt Engineering for Agents
  5. Permissions, Guardrails, and Human Oversight

Chapter 16: Guardrails, Safety, and Control

  1. Why Agents Need Guardrails
  2. Input and Output Filtering
  3. Tool-Level Safety
  4. Human-in-the-Loop Patterns
  5. Monitoring and Kill Switches

Chapter 17: Security Risks and Attack Vectors

  1. Prompt Injection Explained
  2. Indirect Prompt Injection
  3. Data Leakage and Privacy
  4. Tool Abuse and Escalation
  5. The Attacker’s View

Chapter 18: Reliability, Evaluation, and Monitoring

  1. Defining Success
  2. Evaluation Frameworks
  3. Red Teaming
  4. Observability
  5. Continuous Improvement

Chapter 19: Real-World Applications

  1. Software Development and Coding Assistants
  2. Research and Analysis
  3. Customer Service and Support
  4. Business Operations
  5. Healthcare, Finance, and Regulated Industries

Chapter 20: Limitations and the Realistic Horizon

  1. The Hard Problems
  2. Context and Attention Limits
  3. The Reliability Gap
  4. Cost and Latency
  5. What’s Coming Next

Chapter 21: The Bigger Picture: Ethics, Society, and the Future

  1. Jobs, Automation, and Work
  2. Centralization and Power
  3. The Environment
  4. Alignment and Control
  5. Navigating the Transition

Chapter 22: Conclusion: Agentic AI, Clearly

  1. What We Have Learned
  2. Agentic AI in Context
  3. How to Think About Agentic AI Going Forward
  4. The Human Element

References

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