Building Agentic AI - Sinan Ozdemir

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Résumé :
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 Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details....

Biographie:
Sinan Ozdemir ...

Sommaire:

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