← Back to projects

Project

Recursive Deterministic AI Governance: Canonical Twelve-Stage Control Pipeline and Workflow Binding Specification

The canonical twelve-stage governance-control pipeline of recursive deterministic AI governance. Converts a bounded corpus into auditable workflow controls, proceeding from corpus formation and intent embedding through control extraction, workflow binding, and failure harvesting. The endpoint is workflow binding, not recursive fluency.

2026 · Core method documentation · Method designer and author

  • RDAIG pipeline
  • twelve stages
  • control extraction
  • workflow binding
  • failure harvesting
  • core method
  • AI Governance Master Project
The RDAIG Pipeline: twelve-stage governance-control architecture from corpus to auditable controls.

Core method documentation

Method · Core Pipeline — corpus formation through failure harvesting

The RDAIG pipeline is the canonical method specification. Each of the twelve stages answers a distinct governance question and installs a distinct control point: from corpus formation (what is allowed to enter the recursive loop) through intent embedding, multi-model rendering, triangulation, recursive return, control extraction, determinism hardening, deployment binding, logging, and failure harvesting.

The endpoint is not recursive fluency. A control is incomplete until it names the workflow interface where it attaches, the actor who owns it, the evidence artifact that satisfies it, the review interval that sustains it, and the audit path that keeps it contestable.