Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment (Pearson AI Signature Series)

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.