Название: The 0→1 Loop Engineering Playbook (2026 Edition): An AI Agent Engineering System for Building Production-Ready Agents with Tools, Memory, Evals, Feedback Loops, and Workflow Automation
Автор: Valenx Press
Издательство: Valenx Press
Год: 2026
Страниц: 360
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Most engineers build AI agents the same way they build CRUD apps. That is exactly why their agents fail in production. Built from production patterns -- not theory, not demos, not notebooks.

"The REFINE System gave me a repeatable eval process that caught a regression before it hit production. That single save paid for the book 100x over." -- Senior AI Engineer, Anthropic

"Used the SCALE Architecture to redesign our agent infra. Monthly costs dropped from $12K to $4K. My VP noticed." -- Staff Engineer, Stripe

"The scored walkthroughs are exactly what I used to prep for my loop engineering interview. Offer accepted at OpenAI, $320K TC." -- ML Engineer, OpenAI

Software has gone through three major paradigm shifts in the past decade. The first was the move to cloud-native architectures. The second was the adoption of machine learning pipelines. The third, happening right now, is the transition from request-response systems to autonomous loop systems. A loop system does not wait for a human to tell it what to do next. It observes its environment, reasons about what action to take, executes that action, and then checks whether the goal has been met. If not, it loops again. This is fundamentally different from every software paradigm that came before it.

Three trends converged to make loop engineering the critical discipline of 2025-2026:
• Model capability: Models like Claude, GPT-4, and Gemini can now reason well enough to make multi-step decisions without constant human guidance. Before 2023, models were not reliable enough to run autonomously for more than 1-2 steps.
• Tool use: Modern LLM APIs natively support function calling, making it practical to connect reasoning to real-world actions (databases, APIs, file systems, browsers).
• Economic pressure: Companies need to automate knowledge work at scale. A single loop engineer building agent systems can replace the output of 5-10 knowledge workers for routine tasks.

Who this book is for: Software engineers, AI engineers, and ML engineers with production experience who want to build AI agents that run autonomously -- not just demo well.

Who should NOT buy this book: If you are looking for a conceptual overview of LLMs or a tutorial that ends at "hello world," this is not for you. This playbook assumes you ship code and want to ship agents.

They chain prompts in a notebook, add a retry loop, and call it an agent. Real users arrive, the agent hallucinates, costs spike 10x, and the team rebuilds from scratch.

The 0-to-1 Loop Engineering Playbook (2026 Edition) is not a prompt engineering cookbook or theoretical survey -- it is a field-tested engineering system with 91 code blocks, 52 decision tables, and 66 scored walkthroughs covering architecture, evaluation, cost control, and deployment across 20 chapters.

Five proprietary frameworks:

LOOP Framework -- self-correcting agent cycles that detect and fix drift
AGENT Method -- parallel decision engines without deadlocks
CHAIN Protocol -- adaptive task decomposition replacing brittle chains
REFINE System -- evaluations that catch regressions before users do
SCALE Architecture -- distributed agents without losing observability

You will learn how to:

Build 4 production agent types with scored walkthroughs showing weak vs strong implementations
Cut agent operating costs using 13 cost-reduction patterns from Chapter 13
Recover from silent failures using 52 diagnostic tables and structured error recovery
Apply 100 agent design patterns from the reference appendix to production architectures

This playbook is for you if:

Your agents pass local evals but collapse under real user traffic and edge cases
Your team keeps firefighting agent failures instead of shipping new capabilities
You want a structured system instead of scattered blog posts, half-working notebooks, and framework docs that assume you are building a chatbot

If you want to build AI agents with a system that works in production instead of hoping brittle chains hold together, start here.