Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment
Description
Transform Your Business with Intelligent AI to Drive Outcomes
Building reactive AI applications and chatbots is no longer enough. The competitive advantage belongs to those who can build AI that can respond, reason, plan, and execute. Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment takes you beyond basic chatbots to create fully functional, autonomous agents that automate real workflows, enhance human decision-making, and drive measurable business outcomes across high-impact domains like customer support, finance, and research.
Whether you're a developer deploying your first model, a data scientist exploring multi-agent systems and distilled LLMs, or a product manager integrating AI workflows and embedding models, this practical handbook provides tried and tested blueprints for building production-ready systems. Harness the power of reasoning models for applications like computer use, multimodal systems to work with all kinds of data, and fine-tuning techniques to get the most out of AI. Learn to test, monitor, and optimize agentic systems to keep them reliable and cost-effective at enterprise scale.
Master the complete agentic AI pipeline
- Design adaptive AI agents with memory, tool use, and collaborative reasoning capabilities
- Build robust RAG workflows using embeddings, vector databases, and LangGraph state management
- Implement comprehensive evaluation frameworks beyond accuracy, including precision, recall, and latency metrics
- Deploy multimodal AI systems that seamlessly integrate text, vision, audio, and code generation
- Optimize models for production through fine-tuning, quantization, and speculative decoding techniques
- Navigate the bleeding edge of reasoning LLMs and computer-use capabilities
- Balance cost, speed, accuracy, and privacy in real-world deployment scenarios
- Create hybrid architectures that combine multiple agents for complex enterprise applications
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Sinan Ozdemir is an AI expert and entrepreneur with a master's degree in pure mathematics from Johns Hopkins University. He founded Kylie.ai, patented agentic tool use there in 2018, participated in Y Combinator, and exited the company in 2019. Sinan is the author of Quick Start Guide to Large Language Models, Second Edition (Addison-Wesley, 2025), and cohosts the Practically Intelligent podcast. He has created several popular AI courses for Pearson on O'Reilly.
Content
Series Editor Foreword xi
Preface xiii
Acknowledgments xvii
About the Author xix
Part I: Getting Started with Foundations of AI, LLMs, and Experimentation 1
Chapter 1: An Introduction to AI, LLMs, and Agents 3
Introduction 3
The Basics of Large Language Models 3
The Family Tree of LLM Tasks 10
Alignment 10
Prompt Engineering 12
Special LLM Features 17
LLM Workflows 25
AI Agents 25
Conclusion 28
Chapter 2: First Steps with LLM Workflows 31
Introduction 31
Case Study 1: Text-to-SQL Workflow 32
Conclusion 57
Chapter 3: AI Evaluation Plus Experimentation 59
Introduction 59
Evaluating and Experimenting with LLMs 59
Case Study 1, Revisited: The Text-to-SQL Workflow 61
Case Study 2: A "Simple" Summary Prompt 77
Conclusion 83
Part II: Moving the Needle with AI Agents, Workflows, and Multimodality 85
Chapter 4: First Steps with AI Agents and Multi-Agent Workloads 87
Introduction 87
Case Study 3: From RAG to Agents 88
When Should You Use Workflows Versus Agents? 104
Case Study 4: A (Nearly) End-to-End SDR 105
Evaluating Agents 118
Conclusion 121
Chapter 5: Enhancing Agents with Prompting, Workflows, and More Agents 123
Introduction 123
Case Study 5: Agents Complying with Policies Plus Synthetic Data Generation 124
Building Our Policy Bot Agent 127
Case Study 6: Deep Research Plus Content Generation Agentic Workflows 133
Multi-Agent Architectures 141
Case Study 4, Revisited: Adding a Supervisor Agent to Our SDR Team 148
Case Study 7: Agentic Tool Selection Performance 149
Conclusion 157
Chapter 6: Moving Beyond Natural Language: Multimodal and Coding AI 159
Introduction 159
Introduction to Multimodal AI 159
Case Study 8: Image Retrieval Pipelines 168
Case Study 9: Visual Q/A with Moondream 174
Case Study 10: Coding Agent with Image Generation, File Use, and Moondream 176
The Case for Any-to-Any Models 188
Conclusion 191
Part III: Optimizing Workloads with Fine-Tuning, Frameworks, and Reasoning LLMs 193
Chapter 7: Reasoning LLMs and Computer Use 195
Introduction 195
Seven Pillars of Intelligence 195
Case Study 11: Benchmarking Reasoning Models 198
Reasoning Models for ReAct Agents 210
Case Study 12: Computer Use 212
Conclusion 224
Chapter 8: Fine-Tuning AI for Calibrated Performance 225
Introduction 225
Case Study 13: Classification Versus Multiple Choice 227
Case Study 14: Domain Adaptation 245
Conclusion 258
Chapter 9: Optimizing AI Models for Production 261
Introduction 261
Model Compression 261
Case Study 15: Speculative Decoding with Qwen 269
Case Study 16: Voice Bot--Need for Speed 272
Case Study 17: Fine-Tuning Matryoshka Embeddings 277
Case Study
N
+ 1: What Comes Next? 284
Index 287