AI agents can successfully complete tasks and return valid JSON structures while failing to produce usable results. Developers must distinguish between process completion, artifact existence, and user acceptance to avoid misinterpreting technical failures as poor model performance.
A completion gate is necessary to verify that artifacts are not just placeholders or empty files. This gate checks for successful process execution, contract compliance, and the presence of non-empty, valid content before running quality assessments.
Automated tests often fail to account for outdated requirements or misinterpretations. Maintaining a chain of custody for evidence—linking sources to rules, tests, and human acceptance—is essential to ensure that results remain relevant when project contexts change.
Multiple agents producing the same output do not guarantee accuracy, as they may share the same misconceptions. A reliable system should track evidence coverage, highlight conflicting sources, and escalate unresolved issues to human decision-makers.
Separating the roles of executor, validator, and release authority is critical. Automated checks should not be the sole judge of quality; final acceptance must involve a named owner who acknowledges the residual risk of the AI-generated output.
