Thinking in public about where quality goes next.
Frameworks, essays and field observations across Quality Intelligence, AI assurance, Agentic QE and the changing role of evidence.
Four arguments about where quality is going next.
The new pieces move beyond AI-enabled testing into the harder questions: intelligence, confidence, agentic decision design and assurance for systems that think.
Quality Intelligence Is Not AI-Powered Testing
Making testing intelligent is useful. Building an intelligence layer that can reason across quality evidence is a different ambition.
Read article →Release Confidence: The Metric QE Forgot to Build
Quality teams measure almost everything except the decision leaders actually need: do we have enough evidence to release?
Read article →Stop Building Agents. Start Designing Decisions.
Agentic QE should begin with decision boundaries, evidence and authority—not a catalog of AI workers.
Read article →Who Tests the AI? The Assurance Stack for Intelligent Systems
Trust in AI requires evidence across behavior, robustness, safety, grounding, provenance and production.
Read article →Intelligence becomes useful when evidence can be trusted—and action can be governed.
This wave goes deeper into the architecture underneath QI: connected evidence, provenance, autonomous decision boundaries and continuous assurance for intelligent systems.
The Quality Graph
Why Quality Intelligence needs a connected model of changes, dependencies, tests, defects, journeys and outcomes.
Read article →Provenance Is the New Test Evidence
As AI generates more quality conclusions, lineage becomes part of the proof.
Read article →Autonomous Quality Needs a Constitution
Autonomy needs explicit rules for evidence, authority, consequence, reversibility and human control.
Read article →The Confidence Gap
Why intelligent systems need continuous assurance after release, not just pre-release evaluation.
Read article →The theory matters only when the operating model changes.
This wave moves from architecture to execution: how QI should operate, how maturity should be measured, which metrics matter, and how humans and agents should share authority.
From Test Factory to Decision System
How evidence, intelligence, decisions and learning become the four loops of enterprise QI.
Read article →The Quality Intelligence Maturity Model
Five levels from basic visibility to confidence-driven, governed autonomy.
Read article →Stop Measuring Testing. Start Measuring Quality Decisions.
Five metrics for relevance, freshness, calibration, decision speed and information value.
Read article →The Operating Model for Human + Agent Quality
How authority should move between people and agents as confidence and consequence change.
Read article →The intelligence layer needs an architecture of its own.
This wave explores the substrate beneath enterprise QI: reference architecture, evidence contracts, governance control, and a decision fabric that brings intelligence into engineering workflows.
From Signals to Decisions
A five-plane architecture for enterprise Quality Intelligence.
Read article →Quality Data Contracts
The missing foundation for consistent evidence across enterprise systems.
Read article →The QI Control Plane
How to govern agents, evidence, confidence, and autonomous action at scale.
Read article →From Quality Platform to Quality Decision Fabric
Bring intelligence to the engineering decision instead of building another portal.
Read article →The ideas underneath the thesis.
These pieces establish the progression from QE to QI, the role of coordinated agents and the importance of living domain context.
From QE to QI: Why Quality Needs an Intelligence Layer
Automation made testing faster. Intelligence changes what quality can understand and decide.
Read article →Agentic AI for QE: From Assistance to Coordinated Assurance
What changes when agents reason across the lifecycle rather than simply generate test artifacts?
Read article →Domain-Driven Testing Powered by Dynamic RAG
Why test intelligence needs live domain grounding when systems and knowledge change continuously.
Read article →The territory.
A point of view is only useful if it can survive contact with practice.
The Lab is where these ideas become experiments. Quality Intelligence is the model connecting them.