self-verification

You can verify AI project artifacts by running a tri-layer workflow that applies deterministic checks, cross-model adversarial review, and runtime dispatch to validate acceptance criteria, evidence gating, and cross-artifact consistency.

Drift detection identifies scope creep, spec decay, execution skew, and orphaned requirements across PRD, plan, and execution artifacts to prevent silent gaps and maintain cross-artifact consistency during project development.

You can validate execution evidence by applying evidence gating per phase, which checks that every acceptance criterion maps to a verification command with measurable success criteria before allowing progression.

Yes, manual on-demand verification supports cold-path invocation via a command flow to validate tasks without turning verification into an always-on pipeline hook, applying iterative fail/stop logic with cost and iteration ceilings.

Grep-based acceptance criteria coverage heuristics provide a deterministic verification floor but may miss semantic nuances, relying on cross-model adversarial review and runtime dispatch to catch deeper cross-artifact consistency and safety gaps.