Runtime Governance Engineering is the discipline of ensuring AI systems perform consistently and correctly in production environments.
Unlike traditional software testing, AI systems require evaluation across probabilistic outputs, edge cases, and evolving data distributions.
Key pillars of AI reliability:
1. **Measurable Success Criteria** — Define what "correct" looks like before deployment. Without acceptance criteria, you cannot measure improvement.
2. **Continuous Evaluation** — Run automated test suites against production data. Catch regressions before users do.
3. **Failure Mode Analysis** — Categorize errors by type (hallucinations, policy violations, tool misuse) and prioritize fixes by impact.
4. **Human-in-the-Loop Calibration** — Use expert reviewers to validate evaluator accuracy and refine rubrics over time.
5. **Governance Artifacts** — Document controls, maintain audit trails, and capture evidence for compliance requirements.
The goal is not perfection—it is measurable, improvable reliability with clear accountability.