The constraint chain that determines the ceiling for your AI governance quality.
Every regulated industry has measurement standards. Banking has Basel III capital ratios. Manufacturing has defects per million opportunities. Aviation has incident rates per flight hour. AI governance has... nothing.
The existing approach to AI governance is qualitative: checklists, maturity models, and binary compliance gates. These tell you whether you tried to govern AI. They do not tell you how good your governance actually is.
Corvair's patent-pending system introduces three quantitative sigma dimensions that together determine the quality ceiling for any AI-governed operation.
Data Sigma measures the quality of information flowing into AI agents across five dimensions:
Typical starting points: Raw enterprise data typically scores below 3.5 sigma. Curated master data achieves 4–5 sigma. Most organisations overestimate their data quality because they measure availability, not accuracy.
Why it matters: An agent operating on 2-sigma data cannot produce 4-sigma outcomes, regardless of how sophisticated its reasoning is. Data quality is the floor that everything else stands on.
Process Sigma measures how consistently an agent produces correct outputs when given identical inputs and instructions. This is the repeatability problem.
LLM-based agents currently operate at approximately 1–1.5 sigma due to:
The measurement approach: Run identical tasks N times, score each output against a defined rubric, and calculate the defect rate. A "defect" is any output that fails to meet the acceptance criteria (not just catastrophic failures, but any deviation from the defined standard).
Why it matters: Process Sigma reveals the true reliability of your AI operations. An agent that produces correct output 90% of the time sounds acceptable until you realise that across 1,000 daily invocations, you are generating 100 defective outputs every day.
Agent Sigma measures the quality of outcomes when multiple agents work together. It captures compounding effects that do not appear when measuring individual agents in isolation.
The key constraint: Agent Sigma can never exceed Process Sigma. When multiple agents coordinate, individual measurement errors compound. If Agent A's output feeds Agent B's input, and both operate at 3 sigma, the combined quality is lower than 3 sigma because errors cascade.
What it captures:
These three dimensions are not independent metrics. They form a cascading constraint where lower-tier quality caps all higher tiers:
Process Sigma ≤ Data Sigma (can't out-execute bad data)
Agent Sigma ≤ Process Sigma (coordination can't fix unreliable agents)
Overall System Quality = min(Data Sigma, Process Sigma, Agent Sigma)
Practical implication: Investing in better models (improving Process Sigma) while ignoring data quality (Data Sigma) produces no measurable improvement in overall system quality. The weakest link determines the ceiling.
The improvement path: Always start with Data Sigma. Clean, validated, timely input data is the single highest-leverage investment in AI governance quality. Then address Process Sigma through structured prompting, deterministic tool use, and validation checkpoints. Agent Sigma improves last, as a natural consequence of the other two.
| Sigma Level | Defects per Million | Yield | Typical Application |
|---|---|---|---|
| 6σ | 3.4 | 99.99966% | World-class manufacturing, aerospace |
| 5σ | 233 | 99.977% | Precision medical devices |
| 4σ | 6,210 | 99.379% | Financial transaction processing |
| 3σ | 66,807 | 93.32% | Average industrial process |
| 2σ | 308,538 | 69.15% | Current enterprise data quality |
| 1–1.5σ | 500,000–690,000 | 31–50% | Current LLM-based AI agents |
Most AI agent deployments operate at quality levels that manufacturing abandoned decades ago.
The risk that agentic processes lose repeatability over time due to model drift or environment changes.
How measurement errors and execution uncertainty multiply in multi-agent coordination chains.
The failure mode where lower-tier quality issues propagate upward through the constraint chain.
Identify the weakest link in your AI governance chain with a quantitative Readiness Assessment.
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