Artificial Intelligence Operational Behaviour Evaluation Standards
AIOBES is the only testing standard and the only set of methodologies that satisfy EU AI Act behavioural evaluation requirements.
AIOBES is the first comprehensive behavioural evaluation standard for Artificial Intelligence systems. It focuses on how AI actually behaves when people use it – in real tasks, real workflows and real conversations.
AIOBES evaluates operational behaviour rather than isolated prompt testing. It identifies behavioural failures that create regulatory, legal, operational and reputational exposure, and provides a structured way to detect, document and evidence these behaviours.
The AIOBES Foundational Paper defines the behavioural domain, evidentiary structure and operational methodologies that make AIOBES a governance‑grade standard. This section also provides resources for standards bodies and regulators seeking to incorporate AIOBES into external standards, governance frameworks or regulatory instruments. You can read the foundational paper or access the formal integration specification below.
AIOBES evaluates the operational behaviour of AI systems used in real tasks, workflows and interactions. It applies to general purpose AI systems, conversational AI, agentic systems, workflow automation, multimodal systems and domain specific AI used in regulated environments.
AIOBES does not evaluate model architecture, training data, internal mechanisms or synthetic benchmark performance. It evaluates only operational behaviour: how the system behaves when people use it.
AIOBES evaluates AI systems across behavioural dimensions that define what must be examined during behavioural testing. These dimensions identify the areas where stability, instability and contamination typically occur.
AIOBES is designed to detect and document behavioural issues clearly and without ambiguity whenever they occur.
The AIOBES standard is built from six core components. Each provides a specific structural function within the overall framework.
These components form the structural backbone of AIOBES and provide the foundation for behavioural evaluation, documentation and compliance.
AIOBES focuses on behavioural failure modes that appear under real use such as:
These failure modes directly cause workflow contamination - where incorrect, unsafe or non-compliant outputs spread through processes and systems.
When these failure modes occur, organisations face:
AIOBES documents these effects clearly, repeatably and without ambiguity.
AIOBES uses five behavioural criteria to assess how an AI system performs under real operational conditions. These criteria define the behavioural areas where stability, instability, alignment, misalignment and contamination are observed and documented.
AIOBES uses three evidence categories:
Operational concepts within AIOBES describe the conditions under which behavioural evaluation takes place. They outline the operational environment an AI system is expected to perform in and the behavioural boundaries it must maintain across variations in user intent, context and load.
These concepts identify the areas that form the foundation of operational behavioural evaluation. They include behavioural stability, operational boundaries, drift behaviour, load conditions, interaction space and the generation of operational behavioural evidence. These areas will be developed into the full operational evaluation model.
AIOBES will expand these operational concepts into a dedicated operational section. This will define the complete operational framework used to assess AI behaviour under real or realistically simulated conditions.
AIOBES compliance is demonstrated through captured evidence showing how the system behaves under real operational conditions.
AIOBES provides behavioural evidence required under multiple EU AI Act obligations including transparency, risk classification, behavioural stability, misuse prevention, safety documentation and post-market monitoring.
AIOBES does not replace legal compliance. It provides the behavioural evidence required to demonstrate it.
AIOBES uses two operational methodologies to expose how AI behaves under real conditions. They show how specific failure modes lead directly to workflow contamination, workflow collapse and the resulting regulatory, legal, operational and reputational exposure.
Evaluates how AI behaves during long-form work using real tasks, real documents and real operational content. It exposes long-form collapse where the AI loses structure, drifts from requirements, rewrites instead of editing, contaminates the work or breaks the task over time.
It shows whether the AI can maintain stability, alignment and continuity across extended, multi-step real-world work without introducing errors, hallucinations or workflow contamination.
Evaluates AI behaviour across real conversational interactions - text, verbal and multimodal. It examines how the system responds under varied interaction patterns, phrasing, formats and multimodal triggers.
It reveals whether the AI maintains coherence, correctness and alignment when engaged through natural user communication, and whether conversational instability contaminates downstream workflows.
Covers long-form work collapse, real task evaluation and contamination detection in extended operational work.
Available here: https://leanpub.com/llm-inquisitor
Covers behavioural evaluation across real conversational interactions and how conversational instability contaminates downstream workflows.
Available here: coming soon to Leanpub
These guides form the baseline for applying AIOBES correctly.
Practical training for applying AIOBES in real workflows and real interactions. Focused on operational execution.
Formal practitioner certification aligned to the two methodologies. Validates correct application of AIOBES and production of audit-ready behavioural evidence.
Organisational compliance framework based on AIOBES. Provides a structured way to demonstrate behavioural evaluation controls to regulators.