Foundations of Agentic AI for Retail
Concepts, Technologies, and Architectures for Autonomous Retail Systems
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Master Retail's Autonomous Future - Foundations of Agentic AI for Retail (Full-Color Edition) This book is the definitive, end-to-end playbook showing you how to design, code, and deploy autonomous agents that think, learn, and act in real time-transforming every aspect of your retail business. What makes this book indispensable? - Full¿Color Visuals: 75+ diagrams, flowcharts, and architecture blueprints rendered in vivid color, making complex concepts, data flows, and decision loops crystal-clear. - 50+ Real¿World Retail Use Cases: Explore detailed blueprints and discussions covering demand...
Master Retail's Autonomous Future - Foundations of Agentic AI for Retail (Full-Color Edition) This book is the definitive, end-to-end playbook showing you how to design, code, and deploy autonomous agents that think, learn, and act in real time-transforming every aspect of your retail business. What makes this book indispensable? - Full¿Color Visuals: 75+ diagrams, flowcharts, and architecture blueprints rendered in vivid color, making complex concepts, data flows, and decision loops crystal-clear. - 50+ Real¿World Retail Use Cases: Explore detailed blueprints and discussions covering demand forecasting, dynamic pricing, conversational merchandising, autonomous store operations, supply chain optimization, and many more-illustrating agent capabilities across the retail landscape. - 28 Code Examples: Get hands-on with complete, Python listings covering: BDI agents, OODA loops, MDP, Reinforcement Learning pipelines, LLM-powered ReAct chains, MCP-based negotiations, A2A multi-agent orchestration, and beyond. - Retail¿Focused, Industry¿Agnostic Foundations: Master the core math, decision frameworks (like MDPs, Bayesian methods), and reference architectures applicable to any industry, then dive deep into specialized adaptations proven in retail. - Rigor + Cutting¿Edge Tech: Bridge foundational AI (optimization, planning) with the latest breakthroughs: Large Language Models (LLMs), OpenAI's Agents SDK, transformer agents, retrieval-augmented generation (RAG), and modern open multi-agent protocols like Anthropic's MCP and Google's A2A. Inside you'll learn to: 1. Architect autonomous retail systems using layered reference models, event-driven patterns, and API-first design. 2. Orchestrate multi-agent ecosystems that collaborate, negotiate, and self-optimize across pricing, supply chain, marketing, and customer service. 3. Embed Large Language Models as reasoning engines for complex decision-making and natural-language interactions. 4. Implement robust feedback loops & guardrails ensuring agents are safe, explainable, and aligned with AI governance standards. 5. Scale from Proof-of-Concept to enterprise deployment with proven CI/CD pipelines, observability dashboards, and effective rollout strategies. Who should read this book? - Retail Executives & Strategists: Gain a clear, actionable roadmap for AI-driven transformation and competitive advantage. - Software Architects & ML Engineers: Acquire hands-on guidance to design and build next-generation agentic platforms. - Researchers & Advanced Students: Use this as a rigorous yet practical reference on developing and deploying autonomous systems. - AI Enthusiasts: Get a front-row seat to the convergence of LLMs, computer vision, causal inference, and sensor networks in the dynamic world of retail. Why buy now? The retail winners of tomorrow are moving today-transitioning from siloed analytics to fully autonomous, continuously learning agentic ecosystems. Whether you're reinventing an established brand or launching the next disruptor, this comprehensive, full-color guide delivers the complete toolkit. About Author Dr. Fatih Nayebi is VP of Data & AI at ALDO Group, steering the retailer's AI strategy and product innovation. A post-doctoral specialist in Machine Learning and HCI, he has spent six years at McGill University teaching Data Science, ML Engineering, Deep Learning, and Agentic AI.
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