Applied AI for Building Intelligent Systems

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.

Sinan Ozdemir is an AI expert, educator, and entrepreneur with an master’s degree in pure mathematics from Johns Hopkins University, where he also lectured. He founded and exited Kylie.ai where he patented Agentic tool use in 2018, served as the Director of Data Science at the acquitting company for many years, and went through Y Combinator. Sinan has authored multiple practical AI books and popular video courses for O’Reilly, Pearson, and others, and co-hosts the Practically Intelligent podcast. His Quick Start Guide to Large Language Models, Second Edition, is a leading resource on AI and LLMs.