Foundations of Agentic AI for Retail (Dr. Fatih Nayebi) (Z-Library)

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Concepts, Technologies, and Architectures for Autonomous Retail Systems Copyright © 2025 Dr. Fatih Nayebi. Edition: First (v1.1) Publication Date: May 5, 2025 ISBN: 978-1-0694226-0-6 Publisher: Gradient Divergence Location: Montréal, QC, Canada All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means—including photocopying, recording, or other electronic or mechanical methods—without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests or inquiries, please contact the publisher at: contact@gradientdivergence.com While every precaution has been taken in the preparation of this book, neither the author nor the publisher assumes any liability for errors or omissions, or for damages resulting from the use of the information contained herein.

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Code Repository All code examples from this book are available in the GitHub repository at https://github.com/gradient-divergence/agentic-retail-foundations. Community To join discussions, access additional resources, or participate in Agentic AI projects, visit the Gradient Divergence community at gradientdivergence.com.

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Dedication To Grace, Arthur, and Oscar — Your boundless curiosity and constant encouragement illuminate every path I take. You remind me daily that knowledge is meant to be shared, and that the ultimate purpose of innovation is to serve humanity. This book is dedicated to you with all my love. May you forever remain the driving force behind my endeavors, inspiring me to dream bigger, work harder, and strive for a better world. To my wonderful wife, Necmiye — Your unwavering support and belief in me have been my greatest strength. Thank you for the patience, support, and love that made this journey possible. To Professor Jean‑Marc Desharnais — Mentor, co‑author, and friend, your unwavering support and visionary guidance set me on the path that led to this work. Your steadfast guidance, collaborative spirit, and faith in my potential continue to shape the researcher—and person— I strive to be. This work stands on the foundation you helped lay, and I dedicate it to you in deep gratitude and respect.

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Epigraph “The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” — Geoffrey Hinton1 “If we’re successful in building truly intelligent systems, we’ll have the biggest opportunity in human history to make the world better for all of humanity. If we fail to build systems aligned with human values, however, we’ll probably have the biggest catastrophe in human history.” — Stuart Russell2 “The reinforcement learning problem is the AI problem, if you think AI is about an agent. An agent needs to interact with an environment, and learn from its interactions how to improve itself.” — Richard S. Sutton3 These quotes from AI pioneers frame the profound relationship between articial intelligence and humanity. They highlight both the immense potential and critical challenges in developing Agentic AI systems that benet society. As you explore this book, consider how the foundational principles of Agentic AI must be shaped by human values to create retail systems that augment rather than replace human capabilities.

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Points to Ponder How might Hinton’s analogy about submarines and swimming apply to specic Agentic AI tasks within a retail environment (e.g., inventory management, customer service bots)?

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Considering Russell’s warning, what specic “human values” are most critical to embed in retail AI agents to avoid negative consequences? Based on Sutton’s quote, what kinds of “interactions” might a retail agent learn from in a physical store versus an online store? 1. Georey Hinton: Often called a “Godfather of AI,” known for his pioneering work on articial neural networks and deep learning, particularly backpropagation and Boltzmann machines. Awarded the Turing Award in 2018. 2. Stuart Russell: Leading AI researcher, co-author of the standard textbook “Articial Intelligence: A Modern Approach.” Known for his work on rational agents and his advocacy for AI safety and value alignment. 3. Richard S. Sutton: A key gure in reinforcement learning (RL), co-author of the foundational textbook “Reinforcement Learning: An Introduction.” Known for developing temporal dierence learning and actor-critic methods.

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Foreword By Professor Alain Abran, Ph.D., Ing. Emeritus Professor, Department of Software Engineering and IT École de technologie supérieure (ÉTS), Montréal When I rst met Fatih as a doctoral candidate in software engineering, his curiosity was already leaning toward the then‑nascent eld of machine learning. Back then, discussions of autonomous agents and large‑scale AI systems were still largely conned to research seminars and speculative conferences; few imagined the sweeping industrial impact we witness today. Yet Fatih was convinced—even then—that rigorous engineering principles could (and should) underpin intelligent systems long before “AI” became a ubiquitous business acronym. Over the years we spent together—rst during his Ph.D., co‑supervised with my colleague Jean‑Marc Desharnais, and later while he served as a post‑doctoral researcher in our laboratory—we co‑authored publications that blended empirical measurement with innovative uses of predictive models. Those collaborations armed a shared conviction: software engineering, when anchored in disciplined methods and robust bodies of knowledge, can adapt and thrive even as the underlying technologies evolve at breakneck pace.

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That conviction lies at the heart of Foundations of Agentic AI for Retail. The book you are about to read is not merely a technical manual, though it abounds in architectural blueprints, code examples, and implementation guides. Nor is it purely an industry playbook, though retail leaders will nd it invaluable for translating AI hype into operational advantage. It is, instead, a bridge—between scientic rigor and real‑world applicability, between the enduring principles codied in the SWEBOK and the frontier concepts now reshaping commerce through autonomous agents. A rigorous lineage In my own career, I have argued that software engineering must remain rooted in measurable evidence and systematic knowledge. The Software Engineering Body of Knowledge (SWEBOK) was conceived to provide practitioners with a stable, shared foundation—much as civil engineers rely on structural mechanics or physicians on anatomy. Fatih extends that philosophy into the realm of Agentic AI. From his lucid treatment of Belief‑Desire‑Intention (BDI) models and OODA loops, to his detailed guidance on reinforcement learning pipelines and event‑driven architectures, he demonstrates that even the most sophisticated AI agents can—and must—be engineered with the same care we devote to any critical system. Why retail, why now? Retail may seem, at rst glance, an unlikely vanguard for Agentic AI. Yet few industries present a richer tapestry of real‑time signals—prices, inventories, customer behaviors, supply‑chain events—demanding rapid, decentralized decisions. Fatih’s choice of retail as a proving ground is therefore inspired: it

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exposes every limitation of monolithic, rule‑based software and makes a compelling case for autonomous, collaborative agents governed by clear objectives, guardrails, and feedback loops. Readers will appreciate how seamlessly the book weaves advanced theory with concrete practice. Chapter‑by‑chapter, Fatih moves from foundational concepts to decision‑making frameworks, enabling technologies, multi‑agent coordination, and nally to full end‑to‑end integration—including the ethical and governance considerations that responsible engineers must never overlook. The result is a text that will guide C‑suite executives, software architects, data scientists, and graduate students alike. The human dimension Underlying the algorithms and patterns is Fatih’s conviction that technology ultimately serves human progress. His emphasis on Human‑in‑the‑Loop safeguards, transparency, and rigorous evaluation echoes the broader movement toward responsible AI—an ethos that aligns with the scientic mindset we fostered at ÉTS. I am particularly pleased to see extensive attention given to explainability, accountability, and risk management, ensuring that Agentic AI advances do not outpace our capacity to govern them. A glance toward the horizon Agentic systems will soon permeate domains far beyond retail—healthcare, energy, transportation, public services—wherever complex, dynamic environments require continuous adaptation. The frameworks articulated here will serve as a template for those future applications. More importantly, they

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remind us that even as AI models grow in capability, the disciplines of requirements engineering, measurement, validation, and ethical oversight remain indispensable. Fatih has delivered a timely, authoritative, and engaging work. It is a testament to his evolution from inquisitive graduate student to industry leader and educator, and it reects the very principles we strived to instill: intellectual curiosity, methodological rigor, and an unwavering focus on practical impact. I invite you, the reader, to dig into these pages with both critical attention and creative imagination. May you emerge not only informed but inspired to engineer the next generation of intelligent systems—systems that honor the best traditions of our discipline while venturing boldly into new frontiers. Montréal, April 2025 Alain Abran

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Preface A Meeting of Theory and Practice The retail industry is in a period of unprecedented upheaval, driven by rapid advances in technology and seismic shifts in consumer behavior. As articial intelligence (AI) emerges from research labs and enters the mainstream, retailers grapple with a wave of new possibilities—smart shelves that reorder themselves, personalized promotions that adapt in real time, and automated systems that anticipate trends before they become trends. Yet, for every promising pilot project, there remains a wide chasm between conceptual experimentation and fully realized, at-scale Agentic AI solutions. Over the years, I have observed this tension from two vantage points: the technology sector, where startups and established companies alike innovate at breakneck speed, and the academic world, which rigorously interrogates the underlying theory and ethics of AI. In both spheres, the concept of the “autonomous agent”—a software entity capable of perceiving its environment, reasoning about complex states, and taking decisive action—has sparked keen interest. But while the term “Agentic AI” has found its way into research papers and conference keynotes, the practical guidance for deploying such systems in the dynamic realm of retail remains sparse.

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Why Now? We stand at a pivotal moment. The retail industry faces surging expectations from consumers who demand instant gratication, endless customization, and seamless oine-to-online experiences. Traditional methods—largely reliant on human-driven decision-making and heuristic-based approaches—are buckling under the weight of these expectations. Meanwhile, AI-driven breakthroughs in computer vision, natural language processing, reinforcement learning, and edge computing have given us the technical tools needed to build more adaptive and self-sucient systems. These converging forces have created an urgent need for a unifying, accessible resource that synthesizes the full range of Agentic AI capabilities, from foundational theories to architectural best practices. This book aims to ll that void, oering a step-by-step journey through the fundamentals of agent design, decision frameworks, multi-agent coordination, and end-to-end integrations for real-world retail contexts.

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Who This Book Is For Executives: Understand strategic value, applications (supply chain, CX), and implementation success factors for Agentic AI. Engineers/Scientists: Gain practical architectural insights, explore libraries/code examples, and bridge theory with production-grade AI. Product Managers/Analysts: Grasp the “why” and “how” of agentic systems to align stakeholders and technical feasibility. Academics/Instructors: Find real-world retail AI case studies and deployment examples to connect research to practice. 1. Retail Executives and Decision-Makers If your role involves strategic planning or high-level oversight, you’ll nd clarity here on how Agentic AI can reshape key areas of retail—supply chain optimization, customer experience, and more—while uncovering common pitfalls and strategies for success. 2. Data Scientists and Engineers Technical teams charged with creating or maintaining AI-driven solutions will gain practical insights into architectures, libraries, and coding examples. Think of this as your guide for bridging theoretical AI algorithms with robust, production-grade implementations. 3. Product Managers and Business Analysts As the conduit between technical teams and executive leadership, you need a solid grasp of both the “why” and “how” of deploying agentic systems. This book oers a detailed Quick Guide: What’s In It For You?

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roadmap that will help align stakeholder objectives with technical feasibility. 4. Academic Researchers and Instructors Those teaching or researching AI, multi-agent systems, or retail innovation will nd real-world case studies illustrating how Agentic AI moves from whiteboard concepts to in- store deployments. Scope and Structure A roadmap from first principles to full‑scale deployment

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The book is organised in ve deliberate movements. Each Part builds on the previous one: rst clarifying what Agentic AI is, then how to build it, how to network many agents together, how to harden the solution for production, and nally where all this is heading. Skim linearly for a masterclass, or jump straight to the Part that solves today’s problem.

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Part Chapters Core Question Value Promise Key Takeaways I – Foundations of Agentic AI 1 – 5 What makes an agent “agentic”? Establishes the mathematical and conceptual bedrock —BDI, OODA, Bayesian & causal decision models, MDPs, RL, planning. Readers leave with a rigorous mental model and reference code for single‑agent intelligence. II – Enabling Technologies & Architectures 6 – 7 Which technologies turn theory into capability? Dissects LLMs, vision, sensor fabrics, knowledge graphs, causal engines, and their orchestration inside retail platforms. Blueprint‑level diagrams show how to wire perception, reasoning and action into a cohesive stack. III – Multi‑Agent Systems & Integration 8 – 9 How do many agents collaborate (or compete) at retail scale? Covers MAS topologies, communication protocols (FIPA, MCP, A2A), negotiation, task‑allocation patterns, and end‑to‑end orchestration. Practical code and patterns for stitching agents across supply‑chain, stores, e‑commerce and HQ. IV – Implementation & Ethical Guardrails 10 – 12 How do we ship safely, securely and at enterprise scale? Walks through Dev/Data/MLOps, observability, CI/CD, SRE, privacy, risk, explainability, and Templates and checklists ensure production readiness and responsible

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Part Chapters Core Question Value Promise Key Takeaways regulatory compliance. governance from day one. V – Case Studies & Future Directions 13 – 14 What’s working now, and what’s next? Deep dives into live deployments— inventory, dynamic pricing, customer agents—and surveys federated learning, neuromorphic & quantum horizons. Lessons learned, ROI metrics, and a foresight timeline arm readers for the next decade. A Collaborative Lens on Agentic AI This book is the product of many minds—retail operators, data scientists, ethicists, supply‑chain strategists, software engineers, and academic researchers —who stress‑tested every chapter. Their cross‑disciplinary feedback keeps the material clear whether you care about GPU latency, inventory turns, or governance policy. Agentic AI can only reach its full potential when diverse perspectives work in concert; that principle guided every page that follows. Reading Paths: Find the Chapters That Serve You Best Executives & Business Leaders (CEO / CMO / COO) Skim the opening section of each chapter for high‑level concepts, business impact, and strategic takeaways. Zero‑in on Introduction (Ch 1), Implementation Strategy (Ch 10), Ethical Considerations (Ch 12),

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Case Studies (Ch 13), and Future Directions (Ch 14). The Key Takeaways boxes distill the essence without deep technical detail. Architects & Technical Leaders (CTO / Enterprise Architects) After each chapter’s intro, dive into Agent Architectures (Ch 2), Decision Frameworks (Ch 3‑5), Core Technologies (Ch 6‑7), Multi‑Agent Systems (Ch 8‑9), and Implementation Workows (Ch 10). Pay special attention to system diagrams, integration patterns, and Limitations & Challenges call‑outs to pre‑empt real‑world hurdles. Mathematicians & Researchers Focus on the formal treatments in Chapters 2‑7 and Appendix A. These cover mathematical foundations, proofs, and guarantees that link retail applications to rigorous theory. The extensive References section will steer further scholarship. Engineers & Developers Head straight for the hands‑on material in Chapters 2‑10. Complete, runnable code listings, framework walk‑throughs, and MLOps blueprints provide everything you need to build, test, and ship agentic systems. Each chapter follows a consistent arc—Business Context -> Theory -> Hands‑on Implementation -> Key Takeaways—so you can choose your depth of engagement and still stay on the narrative rail. My Journey and Aspirations My path to writing Foundations of Agentic AI for Retail has been shaped by a career spent at the crossroads of enterprise technology, academic research, and

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practical product development. As Head of Data, Analytics, and AI at a global retailer, I have navigated large-scale deployment challenges, from securing organizational buy-in to wrestling with integration complexities. As a Faculty Lecturer, I have found joy in making advanced AI concepts accessible to students and professionals who arrive with diverse backgrounds yet share a zeal for innovation. This book is both a testament to the road traveled and a roadmap for the journey yet to come. My hope is that these pages demystify Agentic AI and act as a catalyst—moving you from proofs‑of‑concept to production, from tactical wins to strategic transformation. Done well, autonomous agents don’t replace humans; they free us to focus on creativity and strategy. Above all, I hope that by blending practical guidance with deep theoretical underpinnings, Foundations of Agentic AI for Retail can be the catalyst that propels you from proofs-of-concept to transformative, industry-leading solutions. The future of retail, I believe, rests on the shoulders of autonomous agents that complement human expertise rather than substitute it—creating a world where intelligent systems augment, rather than eclipse, our innate potential. Code Repository and Interactive Notebooks All code examples from this book are available in the GitHub repository at https://github.com/gradient-divergence/agentic-retail-foundations. The repository includes marimo notebooks for each chapter, allowing you to interact