[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzgxMzg","title","AI Pipeline Governance Handbook","state","published","contactAuthorMethod","email","aboutText","

AI Pipeline Governance Handbook is a practical guide to building deterministic, auditable AI systems using gate-based runtime control.

The book introduces the Phionyx pipeline governance architecture: a structured AI runtime model built around canonical block execution, safety gates, capability profiles, audit trails, telemetry and deployment patterns.

It covers the 46-block canonical pipeline, gate-based safety enforcement, policy bypass without removing safety code, custom pipeline blocks, audit trail integrity, monitoring, telemetry and production deployment.

The core idea is simple: LLM output should be treated as measurement, not truth. Instead of wrapping a model call and hoping for safe behavior, this handbook shows how to place the model inside a governed pipeline with explicit checkpoints before and after the cognitive layer.

This book is written for AI/ML engineers, technical leads, startup CTOs and product teams moving from prompt-based LLM applications toward pipeline-based, auditable AI systems.

It is not a legal compliance manual, certification program or substitute for domain-specific safety review.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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