methodical

To prevent AI from skipping evidence during an audit report, enforce step-by-step completeness discipline requiring explicit scope counting, step-level evidence receipts, and an explicit completeness statement rather than relying on end-only summaries.

AI agents replace complete findings with summaries that hide omissions due to common failure modes like summary-first fabrication and assumed verification. Applying anti-pattern detection for intellectual honesty blocks these silent scope reductions.

Generation and criticism separation for agent reliability is a structured self-critique process that runs after initial output generation to catch what was omitted or softened, ensuring intellectual honesty in completeness verification.

To verify acceptance criteria without assumed verification, apply methodical execution that requires observable evidence for each step, blocks deferral as completion, and mandates an explicit completeness statement naming covered items and exclusions.

You need step-level evidence receipts for diagnostic health checks when completeness and intellectual honesty are required over brevity, ensuring every step includes observable evidence instead of filtered or assumed verification.

Intellectual honesty techniques are designed for audits, health checks, and verification against acceptance criteria where completeness matters more than brevity, making them unsuitable for tasks prioritizing concise summaries over exhaustive findings.