Field notes for quality leaders
Release intelligence, packaged apps, architecture, agentic AI and the pitfalls that slow every test cycle. Filter by topic or search.
Quality has a tool-sprawl problem
Enterprises buy functional, performance, security, test data and other quality tools separately, each with its own model of the same app. The case for one.
Deterministic execution, AI only at authoring
Where AI sits in a test architecture decides whether results can be reproduced and audited. A design that uses AI to author and a compiled plan to run.
Plain English needs a grammar
Natural-language tests drift into ungoverned prose without a schema. Twelve verbs, quoted targets and a parser keep them readable and exact.
One app model beats eight tool repositories
Each quality tool keeps its own description of your app, and they drift apart. Why a single shared model makes change, impact and evidence tractable.
The runtime AI tax
Runtime-AI test tools call a model on every step of every run. How that cost compounds, what it hides, and where AI pays for itself.
Reference architecture: release-aware QE
The parts of a release-aware quality practice: release intake, one app model, rule-based impact, deterministic engines, a signed quality view and CI hooks.
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