Field Engineering: A Scientific Discipline for Designing the Fields Through Which Intelligence Becomes Action by Rogério Figurelli
English | May 13, 2026 | ISBN: N/A | ASIN: B0GX2TNS65 | 308 pages | EPUB | 1.33 Mb
Written by Rogério Figurelli, author of the Field Engineering Framework and a pioneer of Large Language Fields (LLFs), this series is grounded in the idea that the next phase of AI reliability will not be solved by better models alone. It will require engineered environments, governance layers, evidence paths, permissions, release gates, and return mechanisms that determine when intelligent outputs deserve to become trusted decisions or actions.
The book begins from a simple but decisive insight: a model can generate a candidate, but only a field can decide what that candidate is allowed to become.
As AI systems become more capable, the central risk is no longer only that they may produce wrong answers. The deeper risk is that useful, fluent, well-supported outputs may be trusted too early. A generated answer may be mistaken for evidence. A cited response may be mistaken for authority. A tool plan may be mistaken for permission. A passing test may be mistaken for production readiness. Field Engineering gives readers a disciplined way to prevent these wrong promotions.
The book distinguishes Field Engineering from prompt engineering, context engineering, agent engineering, guardrails, observability, and AI governance. Prompts improve behavior. Context improves available information. Agents improve execution. Governance defines oversight.
But Field Engineering defines passage: what the AI was allowed to do, what it was forbidden to do, what status its output has, what blocks stronger promotion, and what receipt supports the returned status.
At the center of the book is the Field Contract: a practical structure for defining purpose, scope, entry conditions, transformations, persistence, promotion gates, HOLD conditions, receipts, return, falsifiers, and retirement. Through this contract, abstract AI governance becomes operational. The system no longer returns only content; it returns status. It can distinguish draft, evidence candidate, review support, action candidate, escalation, rejection, and approval within scope.
The book also develops practical fields for real systems: enterprise RAG, tool-using agents, production readiness, customer resolution, legal interpretation, clinical escalation, hiring fairness, financial boundaries, and public-benefit classification. In each case, the same principle returns: relevance is not authority, tool access is not permission, score is not judgment, and useful support is not sovereignty.
The final movement of the book argues that trustworthy AI will not be built only by improving models. Better models form better candidates, but better fields govern what those candidates become.
Field Engineering is therefore presented as the discipline after better models: the engineering of passage between intelligence and consequence.
This book is recommended for AI architects, enterprise architects, technology leaders, governance teams, product managers, risk professionals, researchers, and builders who want AI systems that are not only powerful, but governable, auditable, corrigible, and responsible.
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