Intelligence at Scale: LLMs, Agents, and the Future of AI

Twelve years of building AI — from founding an AutoML startup before AutoML existed, through the AWS SageMaker launch team and $32 million of ML-driven revenue at Amazon Prime Video, to running two AI companies today. One book for the engineers building production AI agents, not demos.Shashank Agarwal was on the original AWS SageMaker launch team, where he designed and shipped Hyperparameter Optimization. He built ML-powered personalization at Amazon Prime Video in London that generated $32 million in attributable revenue. He founded Hopdata — automated machine learning years before AutoML existed — and today runs Noveum AI (AI agent observability) and API.market (a global marketplace for AI APIs and MCP servers).This is not another AI survey. This is not a hype piece. This is the book a working software engineer picks up once and keeps — the book that takes you from "I use ChatGPT" to "I build, deploy, and operate production AI agents."What you will build and understandFoundations — how transformers, self-attention, tokenization, training, and inference actually work, explained from first principles with 80+ original diagramsPrompt engineering and agents — system prompts as product, tool calling, retrieval-augmented generation (RAG) with vector databases, and the Model Context Protocol (MCP)Multi-agent systems — supervisor, pipeline, swarm, and hierarchical patterns with working code in LangGraph, CrewAI, and AutoGenProduction AI — deployment, evaluation, observability, and the playbook for agents that don't break at 3 AMBeyond text — image, voice, music, and video AI explained at the architecture levelThe AI coding revolution — how Claude Code, Cursor, and agentic coding are rewriting software engineeringThe future — multi-agent companies, AI-on-AI security, and where to place your next five yearsWhat makes this book differentAn operator wrote it, not an academic. Every pattern in this book has run in production — at Amazon scale, at startup scale, and at the messy scale in between.Hard-won, not rehashed. Twelve years of shipping real machine learning systems, not a survey of other people's work.The 2026 stack, not a 2023 retrofit. Claude 4, GPT-5-class models, Gemini 2.5, Claude Code, LangGraph, CrewAI, and the Model Context Protocol — the tools teams are actually shipping with this year, covered the way practitioners use them.Depth without the $60 price tag. 22 chapters from transformer internals to multi-agent production systems. 68,000 words. 80+ original diagrams. The O'Reilly breadth, at a fraction of the O'Reilly price.Who this book is forSoftware engineers and ML practitioners building real AI agentsTechnical founders deciding where to compete in the AI stackSenior engineers and tech leads moving teams to agent-based architecturesComputer science students and self-taught developers ready to go past tutorialsPrerequisites: basic coding experience in any language and some familiarity with linear algebra. No PhD required. No specific framework required. You just need to have built something before."The system prompt is the product. Production AI is a different discipline than demo AI. You can't fix what you can't see."This book is the culmination of a career spent turning research into shipping systems — written down, once, properly, for every engineer building in the age of agents. Read more