Overview

The AI pilot demo went perfectly. The board nodded. The slides looked great. Six months later, nothing has changed in the numbers. If that feels uncomfortably familiar, this book is for you. Most generative AI pilots never turn into systems that move revenue, margin or risk. The problem is rarely the model. It is the gap between a clever demo and a production system trusted enough to touch systems of record. This book is about closing that gap. Production-Grade AI is a practical playbook for leaders and engineers who need AI systems that last. Not hype, not theory: patterns, code and hard lessons from teams who shipped and then had to live with the thing. PART I: EXECUTIVE STRATEGY Learn what to fund, where to start and how to govern it: A 12 week operating plan to move one workflow from demo to EBIT visible results How generative AI and LLMs really work, stripped of vendor spin Why chat interfaces stall and how to redesign workflows end to end How to build agentic ecosystems with clear scopes, safe boundaries and sensible fallbacks Risk tiers, approval gates and Human in the Loop patterns that scale across a portfolio PART II: DELIVERY IN PRACTICE Turn strategy into working systems: LLMs, prompts and tooling that survive contact with real users Moving from assistants that answer questions to agents that take action Architectures that scale: hub and spoke patterns, orchestration and robust tool calling Security, safety and supply chain guidance for the current legal and regulatory landscape Evaluation harnesses, rollout and rollback strategies, and playbooks for going live 20 CHAPTERS The book is structured as 20 chapters arranged in 10 mirrored pairs. Every strategy chapter in Part I is matched with a delivery chapter in Part II, so executives and engineers work from the same map. WHO THIS BOOK IS FOR CEOs and board members who want to fund the right AI work in the right order CTOs and CIOs who need architectures and guardrails they can defend Engineering leaders accountable for uptime, observability and security Product managers who want to redesign workflows around agents, not bolt on another chat box Technical leads who want working Python examples, evaluation rubrics and deployment checklists Inside: working code, decision matrices, architecture diagrams and real case studies from teams that moved beyond the demo and into the numbers.

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