Deep technical analyses, empirical benchmarks, and architectural patterns for teams building with AI agents.
Why language models generate a false sense of feature completeness and how a deterministic requirement-to-test matrix (cdal trace --enforce) guarantees proof.
Hard empirical metrics from a commercial enterprise SaaS: 6 contracts, 47 requirements, 342 intercepted tampering attempts, and zero production regressions.
Why AI developers cannot grade their own work, and how PreToolUse hooks returning exit code 2 physically block test tampering before execution.
Why linters and quality guidelines give a dangerous false sense of security until you prove that every gate is capable of failing red.