Quantifying AI governance quality with the proven methodology that banking COOs and CROs already trust.
Regulatory frameworks demand accuracy and reliability from AI systems, but no existing standard tells you how good your governance actually is. Compliance is binary: pass or fail. That is not how quality works.
Manufacturing solved this problem decades ago. Six Sigma gave every factory floor a universal language for defects, variation, and continuous improvement. Corvair's patent-pending system brings that same discipline to autonomous AI, making agent process quality measurable, governable, and continuously improvable.
This section is a comprehensive resource for enterprise leaders, risk officers, and architects who want to understand how proven quality frameworks apply to the emerging challenge of governing AI agents at scale.
A deep-dive into how Corvair's patent-pending Unified Governance system applies DMAIC, operational waste analysis, and mistake-proofing to autonomous AI agents.
Agent quality is measurable across Data Sigma, Process Sigma, and Agent Sigma. The weakest link sets the system ceiling.
Read Article →How the five phases of continuous improvement operate continuously inside the governance engine's control loop.
Read Article →The five categories of Muda (waste) in AI agent systems, from permission waste to exposure waste.
Read Article →Applying poka-yoke to deployments: blocking non-conformant agents before they reach production.
Read Article →Solving the compound error problem: achieving Six Sigma quality from imperfect individual agents.
Read Article →Browse and download all Six Sigma articles, videos, slide decks, and technical guides in one place.
Browse Resources →| Dimension | What It Measures | Starting Point | Target |
|---|---|---|---|
| Data Sigma | Input quality: completeness, accuracy, timeliness, consistency, validity | 2.0–3.5σ | 4.5σ+ |
| Process Sigma | Agent repeatability and consistency across identical tasks | 1.0–1.5σ | 3.5σ+ |
| Agent Sigma | Multi-agent coordination effects and compounding error | < Process Sigma | 3.0σ+ |
If your organisation is deploying AI agents in regulated environments (banking, insurance, healthcare, legal), you face a measurement gap. Regulators will eventually ask not just whether you govern AI, but how well. Six Sigma gives you a defensible, quantitative answer that auditors already understand.
Quantify your risk exposure with a Six Sigma Readiness Assessment of your agentic workflows.
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