Where ideas are allowed to fail usefully.
Hypotheses, prototypes, patterns and lessons from the edge of Quality Intelligence. The abandoned experiments stay visible because a lab that reports only successes is a marketing page.
Eight questions worth testing.
The Lab is intentionally not a product catalog. Each entry starts with a question, a hypothesis and evidence we would want before declaring the idea useful.
Agentic Quality Intelligence
Can multiple quality agents share context, reason over evidence and coordinate assurance without reducing the human role to approval clicks?
Dynamic RAG for Testing
Can testing intelligence remain relevant when the domain model changes faster than documentation can be maintained?
Predictive Quality
Can change, dependency and historical signals forecast release risk before regression execution begins?
AI Assurance
How should quality be measured when acceptable AI responses can vary and still be valid?
Autonomous Quality
Where should quality autonomy stop—and what evidence should be required before a system acts on its own?
Quality Economics
Can Quality Intelligence show how assurance changes business risk rather than simply reducing testing effort?
The experiment is only interesting if the result can change how quality is practiced.
Start with the research notes in Insights, or explore the Quality Intelligence model these experiments are trying to prove.
Two questions about whether confidence itself can be trusted.
The next experiments focus on calibration and evidence freshness—two properties a real Quality Intelligence system cannot ignore.
Confidence Calibration
Do Strong, Moderate and Weak release-confidence positions correspond to meaningful differences in actual post-release outcomes?
Evidence Freshness
Can technically valid but stale quality evidence create false confidence after the system changes?
Two tests of whether QI can change the economics of assurance.
These experiments move beyond scoring risk and ask whether intelligence can select better assurance actions and safely reduce routine regression breadth.
Next Best Assurance Action
Can QI choose the evidence-gathering action most likely to improve the decision for the lowest cost and time?
Risk-First vs. Regression-First
Can risk-first assurance maintain protection while executing materially less routine regression?
Two tests of whether architecture actually improves intelligence.
Graphs and data contracts should earn their complexity by producing better impact prediction, stronger grounding, and more trustworthy decisions.
Quality Graph vs. Dependency Map
Can connected quality evidence predict release blast radius better than static dependency analysis?
Evidence Contracts for Agent Grounding
Can explicit evidence contracts reduce stale, unsupported, and inconsistent agent conclusions?