Documenting an agent's decision boundaries for auditors necessitates a comprehensive approach to ensure transparency and verifiability in its operational logic. This typically involves detailing the training data and features utilized during model development, alongside the underlying model architecture and decision-making algorithms. Auditors will require explicit clarity on any defined rules, logical flows, and numerical thresholds that dictate the agent's actions or classifications. Furthermore, employing explainability techniques such as SHAP or LIME values can illustrate feature importance and how inputs influence specific outputs, crucial for understanding complex models. Documentation should also clearly outline how the agent handles edge cases, conflicting inputs, or scenarios outside its trained scope to prevent unexpected behavior. Finally, maintaining robust version control for the agent and its documentation, coupled with a system for decision traceability linking specific outcomes back to their input and model state, is paramount for auditability and compliance. More details: https://srpskijezik.org/Home/Link?linkId=https://infoguide.com.ua/