D-POAF® Operating Guide

EAN : 9791098881206

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Édition papier

EAN : 9791098881206

Paru le : 22 août 2026

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Détails

EAN13 : 9791098881206
Date Parution : 22 août 2026
Disponibilite : Disponible
Barème de remise : NS
Nombre de pages : 84
Format : H:229 mm L:152 mm E:5 mm
Poids : 129gr

Résumé

Résumé : D-POAF® Operating Guide, Fourth Edition is a practical guide to proof-oriented, AI-native software engineering. It addresses a central challenge: as AI increasingly participates in software delivery and decision-making, how can teams preserve accountability, governance and verifiable evidence across the lifecycle?

D-POAF, the Decentralized Proof-Oriented AI Framework, complements existing approaches such as Agile, DevOps and MLOps. It structures work around the Wave, a unit of verifiable progress and governance, and develops three proof families: Proof of Delivery, Proof of Value and Proof of Reliability.

The book presents the D-POAF operating model, roles, artifacts, governance mechanisms, autonomy principles and conformance levels. It also introduces decision and investment models including BVS, ERS, PVS, the multidimensional decision matrix and the Optimal Development Path.

A complete reference implementation explores WaveChains, the WaveRegister, cryptographic integrity mechanisms, distributed validation and Monte Carlo simulations.

Designed for engineers, architects, product and portfolio leaders, AI governance professionals, security and compliance teams, researchers and advanced students, the guide shows how AI-enabled engineering can remain governable, reviewable and evidence-driven.
Biographie : Sara Ihsine and Azzeddine Ihsine are co-founders of Inovionix and co-creators of D-POAF®, the Decentralized Proof-Oriented AI Framework. Their research and development work explores AI-native software engineering, human-AI collaboration, proof-oriented systems, adaptive governance, decision and investment models, autonomy, cryptographic verification, simulation, and the design of evolvable software ecosystems. Through D-POAF, they develop frameworks, models and practical tools intended to make AI-enabled engineering more governable, verifiable and adaptable at team, project and organizational scale.