Название: Foundations of Agentic AI for Retail: Concepts, Technologies, and Architectures for Autonomous Retail SystemsАвтор: Fatih Nayebi
Издательство: Gradient Divergence
Год: 2025 (v1.1)
Страниц: 444
Язык: английский
Формат: epub (true)
Размер: 32.2 MB
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.
At its core, Agentic AI refers to AI systems — often called AI agents — that are capable of autonomously performing tasks on behalf of a user or another system by dynamically designing their own workflows and using available tools. It’s important to note that Agentic AI is not just Generative AI with a new name. While Generative AI (like ChatGPT) focuses on producing content in response to prompts, Agentic AI is goal-directed and can operate autonomously over extended periods. Agentic AI systems don’t necessarily require a prompt for each action; they can chain together sequences of decisions and actions to meet a higher-level objective. In other words, Generative AI is often reactive (it does something after you ask), whereas Agentic AI is proactive — it can initiate actions, adjust to changing conditions, and drive processes forward on its own. Agentic AI also tends to incorporate multiple AI techniques (LLMs, traditional algorithms, tools, etc.) to achieve precision in decision-making that pure generative models lack. This means an agentic system might generate content as one step, but it will also make choices, query databases, invoke APIs, or anything else required to reach its goal. In short, Agentic AI systems are designed for autonomous decision-making and action, giving them a novel form of digital agency beyond the capabilities of earlier AI approaches.
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.
All code examples from this book are available in the GitHub repository. Examples in Python.
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.
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