🤖 AI Summary
This study addresses the lack of a systematic evaluation framework for AI governance prompts, which undermines their structural integrity as enforceable norms. To bridge this gap, the work proposes an integrative five-principle assessment framework grounded in computability theory, proof theory, and Bayesian epistemology. The authors conduct a static analysis of 34 AGENTS.md files from GitHub, revealing that 37% of file–model pairs fail to meet the defined threshold for structural integrity. Common deficiencies include missing data categorization and absent evaluation criteria, exposing undocumented gaps in artifact classification. These findings provide both theoretical grounding and empirical evidence for developing automated tools capable of detecting and repairing such deficiencies, thereby advancing the formalization and operationalization of AI governance prompts.
📝 Abstract
AI governance programmes increasingly rely on natural language prompts to constrain and direct AI agent behaviour. These prompts function as executable specifications: they define the agent's mandate, scope, and quality criteria. Despite this role, no systematic framework exists for evaluating whether a governance prompt is structurally complete. We introduce a five-principle evaluation framework grounded in computability theory, proof theory, and Bayesian epistemology, and apply it to an empirical corpus of 34 publicly available AGENTS.md governance files sourced from GitHub. Our evaluation reveals that 37% of evaluated file-model pairs score below the structural completeness threshold, with data classification and assessment rubric criteria most frequently absent. These results suggest that practitioner-authored governance prompts exhibit consistent structural patterns that automated static analysis could detect and remediate. We discuss implications for requirements engineering practice in AI-assisted development contexts, identify a previously undocumented artefact classification gap in the AGENTS.md convention, and propose directions for tool support.