A common failure mode for AI agents is producing validly formatted but empty output, a problem that schema validation alone cannot detect. This occurs when agents return identical list items, placeholder text like 'TODO', or empty containers, all of which pass schema checks but indicate a lack of actual work. To address this, seven measurable signals have been developed to identify mechanical fingerprints of absence, such as uniform lists, stub values, blank containers, truncation markers, low-variety repetition, schema defaults, and first-enum values. These signals categorize output into 'CONTENT_PRESENT', 'THIN_CONTENT', or 'VALID_BUT_EMPTY' verdicts, focusing on verifiable metrics rather than subjective assessments. AI
IMPACT Highlights a critical gap in current AI agent evaluation, potentially leading to more robust and reliable AI systems.
RANK_REASON The item discusses a conceptual problem with AI agent output and proposes a new set of metrics, rather than announcing a new product, research finding, or policy.
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