Artificial Intelligence Operational Behaviour Evaluation Standards (AIOBES)
Principles v1.0
AIOBES is built on behavioural evaluation principles that define how AI systems must be assessed under real operational conditions. These principles ensure that behavioural testing is consistent, repeatable and aligned to real workflows, real interactions and real organisational risk.
These principles apply to all AIOBES methodologies, evidence requirements and evaluation processes.
AI systems must be evaluated under real operational conditions using real tasks, real workflows and real interactions. Synthetic prompts, artificial scenarios and isolated test inputs do not represent operational behaviour and cannot be used as the basis for behavioural evaluation.
AIOBES evaluates only how the system behaves when used. It does not assess model architecture, training data, internal mechanisms or benchmark performance. Operational behaviour is the sole focus.
Behavioural evaluation must identify specific failure modes such as context collapse, hallucinated content, guardrail inconsistency, incorrect refusals, unstable outputs and conversational instability. These failure modes must be documented clearly and without ambiguity.
Behavioural failures must be evaluated in terms of their operational impact, including workflow contamination, workflow collapse, regulatory non-compliance, legal exposure and reputational risk. Behavioural testing must connect system behaviour to real organisational consequences.
All behavioural findings must be supported by clear, reproducible evidence. Evidence must show the behaviour, the conditions under which it occurred and the resulting operational impact. Assertions without evidence are not valid under AIOBES.
AIOBES does not impose ideals such as guaranteed stability or perfect continuity. Instead, it exposes the operational limits within which work can be safely performed, and identifies the risk boundaries that organisations must avoid to prevent workflow contamination and collapse.
AI behaviour must be tested across varied interaction patterns, phrasing, formats and multimodal inputs. Behavioural stability must hold across natural user communication, not only idealised or controlled inputs.
Behavioural findings must be interpreted consistently. AIOBES provides a unified terminology and structured evaluation framework that makes it easier for teams, departments and organisations to communicate clearly about what is happening, why it is happening and how behavioural failures affect workflows.
AIOBES does not perform compliance alignment itself. It provides the behavioural evidence and evaluation tools that organisations need in order to align AI behaviour with regulated workflows, safety requirements and operational constraints.
Behavioural evaluation practices must evolve as AI systems change, as regulatory requirements develop, and as new operational use cases emerge. AIOBES principles guide this evolution and ensure that behavioural testing remains aligned to real-world risk.