SKU/Artículo: AMZ-B0GCFRX9HR
Mastering AI Agents with Small Language Models: Building Local-First, Tool-Using, Autonomous Systems from Scratch (Series on Small Language Models)
Format:
Kindle
Detalles del producto
Disponibilidad:
Fuera de stock
Fuera de stock
Peso con empaque:
0.15 kg
0.15 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- This book is a practical, uncompromising guide to building AI agents that actually work.At a time when most agent frameworks rely on oversized models, opaque abstractions, and fragile prompt chains, this book takes a radically different approach. It shows you how to build reliable, controllable, production-ready AI agents using small language models, local inference, and disciplined system design.This is not a book about prompt tricks or speculative architectures. It is a hands-on engineering guide for developers who want agents that behave predictably, run locally, respect resource limits, and can be trusted to act without supervision.You will learn how to design agents as systems, not conversations. Starting from a minimal observe–reason–act loop, the book methodically builds up real capabilities: structured reasoning, explicit state, short-term and long-term memory, tool use with contracts, incremental planning, self-evaluation, failure recovery, guardrails, and operational hardening. Every concept is backed by concrete, accurate code. Nothing is hand-waved. Nothing is redundant. Each chapter builds directly on the last.The book is written with a clear philosophy: the language model is not the agent. The model proposes, but the system decides. State is explicit. Memory is owned by code. Actions are validated before execution. Failures are visible and recoverable. Determinism, reproducibility, latency control, and safety are treated as first-class concerns from the beginning, not afterthoughts.By the end of the book, you will have a complete blueprint for building local, small-model agents that can reason, plan, act through tools, evaluate their own outputs, recover from partial failure, and run as long-lived processes. You will understand not only what these agents can do, but just as importantly, what they cannot and should not be expected to do.This book is for engineers, architects, and serious practitioners who are tired of demos that collapse in real use. If you want to build AI agents that are efficient instead of bloated, controllable instead of magical, and production-ready instead of experimental, this book was written for you.It does not promise artificial general intelligence. It delivers something far more valuable: agents you can trust.
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